diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py
index 87cb088c2e91e..b76e52acb8be4 100644
--- a/pandas/core/reshape.py
+++ b/pandas/core/reshape.py
@@ -25,6 +25,7 @@
 
 import pandas.core.algorithms as algos
 import pandas.algos as _algos
+from pandas.core.missing import validate_fill_value
 
 from pandas.core.index import MultiIndex, _get_na_value
 
@@ -405,6 +406,9 @@ def _slow_pivot(index, columns, values):
 
 
 def unstack(obj, level, fill_value=None):
+    if fill_value:
+        validate_fill_value(fill_value, obj.values.dtype)
+
     if isinstance(level, (tuple, list)):
         return _unstack_multiple(obj, level)
 
diff --git a/pandas/tests/types/test_missing.py b/pandas/tests/types/test_missing.py
index cab44f1122ae1..bdbebfe01985b 100644
--- a/pandas/tests/types/test_missing.py
+++ b/pandas/tests/types/test_missing.py
@@ -12,7 +12,8 @@
                     DatetimeIndex, TimedeltaIndex, date_range)
 from pandas.types.dtypes import DatetimeTZDtype
 from pandas.types.missing import (array_equivalent, isnull, notnull,
-                                  na_value_for_dtype)
+                                  na_value_for_dtype,
+                                  validate_fill_value)
 
 
 def test_notnull():
@@ -301,3 +302,11 @@ def test_na_value_for_dtype():
 
     for dtype in ['O']:
         assert np.isnan(na_value_for_dtype(np.dtype(dtype)))
+
+
+class TestValidateFillValue(tm.TestCase):
+    # TODO: Fill out the test cases.
+    def test_validate_fill_value(self):
+        # validate_fill_value()
+        # import pdb; pdb.set_trace()
+        pass
diff --git a/pandas/types/missing.py b/pandas/types/missing.py
index e6791b79bf3bd..9d36a38b59c38 100644
--- a/pandas/types/missing.py
+++ b/pandas/types/missing.py
@@ -19,7 +19,10 @@
                      is_object_dtype,
                      is_integer,
                      _TD_DTYPE,
-                     _NS_DTYPE)
+                     _NS_DTYPE,
+                     is_datetime64_any_dtype, is_float,
+                     is_numeric_dtype, is_complex)
+from datetime import datetime, timedelta
 from .inference import is_list_like
 
 
@@ -391,3 +394,30 @@ def na_value_for_dtype(dtype):
     elif is_bool_dtype(dtype):
         return False
     return np.nan
+
+
+def validate_fill_value(value, dtype):
+    """
+    Make sure the fill value is appropriate for the given dtype.
+    """
+    if not is_scalar(value):
+        raise TypeError('"fill_value" parameter must be '
+                        'a scalar, but you passed a '
+                        '"{0}"'.format(type(value).__name__))
+    elif not isnull(value):
+        if is_numeric_dtype(dtype):
+            if not (is_float(value) or is_integer(value) or is_complex(value)):
+                raise TypeError('"fill_value" parameter must be '
+                                'numeric, but you passed a '
+                                '"{0}"'.format(type(value).__name__))
+        elif is_datetime64_any_dtype(dtype):
+            if not isinstance(value, (np.datetime64, datetime)):
+                raise TypeError('"fill_value" parameter must be a '
+                                'datetime, but you passed a '
+                                '"{0}"'.format(type(value).__name__))
+        elif is_timedelta64_dtype(dtype):
+            if not isinstance(value, (np.timedelta64, timedelta)):
+                raise TypeError('"value" parameter must be '
+                                'a timedelta, but you  passed a '
+                                '"{0}"'.format(type(value).__name__))
+    # if object dtype, do nothing.