diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index 83a70aa34fccf..e34330f18a52d 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -245,6 +245,7 @@ Bug Fixes
 - Bug in ``TimedeltaIndex`` addition where overflow was being allowed without error (:issue:`14816`)
 - Bug in ``DataFrame`` construction in which unsigned 64-bit integer elements were being converted to objects (:issue:`14881`)
 - Bug in ``astype()`` where ``inf`` values were incorrectly converted to integers. Now raises error now with ``astype()`` for Series and DataFrames (:issue:`14265`)
+- Bug in ``DataFrame(..).apply(to_numeric)`` when values are of type decimal.Decimal. (:issue:`14827`)
 - Bug in ``describe()`` when passing a numpy array which does not contain the median to the ``percentiles`` keyword argument (:issue:`14908`)
 
 
diff --git a/pandas/lib.pyx b/pandas/lib.pyx
index b09a1c2755a06..548a96780d37a 100644
--- a/pandas/lib.pyx
+++ b/pandas/lib.pyx
@@ -313,6 +313,7 @@ def isscalar(object val):
     - instances of datetime.datetime
     - instances of datetime.timedelta
     - Period
+    - instances of decimal.Decimal
 
     """
 
@@ -325,7 +326,8 @@ def isscalar(object val):
             or PyDate_Check(val)
             or PyDelta_Check(val)
             or PyTime_Check(val)
-            or util.is_period_object(val))
+            or util.is_period_object(val)
+            or is_decimal(val))
 
 
 def item_from_zerodim(object val):
diff --git a/pandas/src/inference.pyx b/pandas/src/inference.pyx
index 5ac2c70bb1808..a8b694d7ba008 100644
--- a/pandas/src/inference.pyx
+++ b/pandas/src/inference.pyx
@@ -1,4 +1,5 @@
 import sys
+from decimal import Decimal
 cimport util
 from tslib import NaT, get_timezone
 from datetime import datetime, timedelta
@@ -28,6 +29,10 @@ def is_bool(object obj):
 def is_complex(object obj):
     return util.is_complex_object(obj)
 
+
+def is_decimal(object obj):
+    return isinstance(obj, Decimal)
+
 cpdef bint is_period(object val):
     """ Return a boolean if this is a Period object """
     return util.is_period_object(val)
@@ -673,6 +678,9 @@ def maybe_convert_numeric(object[:] values, set na_values,
         elif util.is_complex_object(val):
             complexes[i] = val
             seen_complex = True
+        elif is_decimal(val):
+            floats[i] = complexes[i] = val
+            seen_float = True
         else:
             try:
                 status = floatify(val, &fval, &maybe_int)
diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py
index f9647721e3c5b..f808abcda9418 100644
--- a/pandas/tools/tests/test_util.py
+++ b/pandas/tools/tests/test_util.py
@@ -2,6 +2,7 @@
 import locale
 import codecs
 import nose
+import decimal
 
 import numpy as np
 from numpy import iinfo
@@ -208,6 +209,46 @@ def test_numeric(self):
         res = to_numeric(s)
         tm.assert_series_equal(res, expected)
 
+        # GH 14827
+        df = pd.DataFrame(dict(
+            a=[1.2, decimal.Decimal(3.14), decimal.Decimal("infinity"), '0.1'],
+            b=[1.0, 2.0, 3.0, 4.0],
+        ))
+        expected = pd.DataFrame(dict(
+            a=[1.2, 3.14, np.inf, 0.1],
+            b=[1.0, 2.0, 3.0, 4.0],
+        ))
+
+        # Test to_numeric over one column
+        df_copy = df.copy()
+        df_copy['a'] = df_copy['a'].apply(to_numeric)
+        tm.assert_frame_equal(df_copy, expected)
+
+        # Test to_numeric over multiple columns
+        df_copy = df.copy()
+        df_copy[['a', 'b']] = df_copy[['a', 'b']].apply(to_numeric)
+        tm.assert_frame_equal(df_copy, expected)
+
+    def test_numeric_lists_and_arrays(self):
+        # Test to_numeric with embedded lists and arrays
+        df = pd.DataFrame(dict(
+            a=[[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1]
+        ))
+        df['a'] = df['a'].apply(to_numeric)
+        expected = pd.DataFrame(dict(
+            a=[[3.14, 1.0], 1.6, 0.1],
+        ))
+        tm.assert_frame_equal(df, expected)
+
+        df = pd.DataFrame(dict(
+            a=[np.array([decimal.Decimal(3.14), 1.0]), 0.1]
+        ))
+        df['a'] = df['a'].apply(to_numeric)
+        expected = pd.DataFrame(dict(
+            a=[[3.14, 1.0], 0.1],
+        ))
+        tm.assert_frame_equal(df, expected)
+
     def test_all_nan(self):
         s = pd.Series(['a', 'b', 'c'])
         res = to_numeric(s, errors='coerce')
diff --git a/pandas/tools/util.py b/pandas/tools/util.py
index b50bf9dc448bc..daecf3d093680 100644
--- a/pandas/tools/util.py
+++ b/pandas/tools/util.py
@@ -5,7 +5,10 @@
                                  is_numeric_dtype,
                                  is_datetime_or_timedelta_dtype,
                                  is_list_like,
-                                 _ensure_object)
+                                 _ensure_object,
+                                 is_decimal,
+                                 is_scalar as isscalar)
+
 from pandas.types.cast import _possibly_downcast_to_dtype
 
 import pandas as pd
@@ -173,7 +176,9 @@ def to_numeric(arg, errors='raise', downcast=None):
             values = arg.values
     elif isinstance(arg, (list, tuple)):
         values = np.array(arg, dtype='O')
-    elif np.isscalar(arg):
+    elif isscalar(arg):
+        if is_decimal(arg):
+            return float(arg)
         if is_number(arg):
             return arg
         is_scalar = True
diff --git a/pandas/types/inference.py b/pandas/types/inference.py
index 35a2dc2fb831b..d2a2924b27659 100644
--- a/pandas/types/inference.py
+++ b/pandas/types/inference.py
@@ -18,6 +18,8 @@
 
 is_scalar = lib.isscalar
 
+is_decimal = lib.is_decimal
+
 
 def is_number(obj):
     return isinstance(obj, (Number, np.number))