diff --git a/doc/source/whatsnew/v1.3.1.rst b/doc/source/whatsnew/v1.3.1.rst
index 838b7e5fc1a8b..099d818236bd2 100644
--- a/doc/source/whatsnew/v1.3.1.rst
+++ b/doc/source/whatsnew/v1.3.1.rst
@@ -28,7 +28,7 @@ Fixed regressions
 - Fixed regression for ``SettingWithCopyWarning`` displaying incorrect stacklevel (:issue:`42570`)
 - Fixed regression for :func:`merge_asof` raising ``KeyError`` when one of the ``by`` columns is in the index (:issue:`34488`)
 - Fixed regression in :func:`to_datetime` returning pd.NaT for inputs that produce duplicated values, when ``cache=True`` (:issue:`42259`)
-
+- Fixed regression in :meth:`SeriesGroupBy.value_counts` that resulted in an ``IndexError`` when called on a Series with one row (:issue:`42618`)
 
 .. ---------------------------------------------------------------------------
 
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 88d1baae86467..a6be85bf2be2a 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -758,7 +758,7 @@ def apply_series_value_counts():
         # new values are where sorted labels change
         lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
         inc = np.r_[True, lchanges]
-        if not len(lchanges):
+        if not len(val):
             inc = lchanges
         inc[idx] = True  # group boundaries are also new values
         out = np.diff(np.nonzero(np.r_[inc, True])[0])  # value counts
diff --git a/pandas/tests/groupby/test_value_counts.py b/pandas/tests/groupby/test_value_counts.py
index 8bb07b7163f2e..54f672cb69800 100644
--- a/pandas/tests/groupby/test_value_counts.py
+++ b/pandas/tests/groupby/test_value_counts.py
@@ -122,23 +122,27 @@ def test_series_groupby_value_counts_with_grouper():
     tm.assert_series_equal(result, expected)
 
 
-def test_series_groupby_value_counts_empty():
+@pytest.mark.parametrize("columns", [["A", "B"], ["A", "B", "C"]])
+def test_series_groupby_value_counts_empty(columns):
     # GH39172
-    df = DataFrame(columns=["A", "B"])
-    dfg = df.groupby("A")
+    df = DataFrame(columns=columns)
+    dfg = df.groupby(columns[:-1])
 
-    result = dfg["B"].value_counts()
-    expected = Series([], name="B", dtype=result.dtype)
-    expected.index = MultiIndex.from_arrays([[]] * 2, names=["A", "B"])
+    result = dfg[columns[-1]].value_counts()
+    expected = Series([], name=columns[-1], dtype=result.dtype)
+    expected.index = MultiIndex.from_arrays([[]] * len(columns), names=columns)
 
     tm.assert_series_equal(result, expected)
 
-    df = DataFrame(columns=["A", "B", "C"])
-    dfg = df.groupby(["A", "B"])
 
-    result = dfg["C"].value_counts()
-    expected = Series([], name="C", dtype=result.dtype)
-    expected.index = MultiIndex.from_arrays([[]] * 3, names=["A", "B", "C"])
+@pytest.mark.parametrize("columns", [["A", "B"], ["A", "B", "C"]])
+def test_series_groupby_value_counts_one_row(columns):
+    # GH42618
+    df = DataFrame(data=[range(len(columns))], columns=columns)
+    dfg = df.groupby(columns[:-1])
+
+    result = dfg[columns[-1]].value_counts()
+    expected = df.value_counts().rename(columns[-1])
 
     tm.assert_series_equal(result, expected)