diff --git a/doc/source/whatsnew/v0.25.0.rst b/doc/source/whatsnew/v0.25.0.rst
index 506b0b7a850e5..2b04a58c4b9da 100644
--- a/doc/source/whatsnew/v0.25.0.rst
+++ b/doc/source/whatsnew/v0.25.0.rst
@@ -403,6 +403,7 @@ Groupby/Resample/Rolling
 - Bug in :meth:`pandas.core.groupby.GroupBy.idxmax` and :meth:`pandas.core.groupby.GroupBy.idxmin` with datetime column would return incorrect dtype (:issue:`25444`, :issue:`15306`)
 - Bug in :meth:`pandas.core.groupby.GroupBy.cumsum`, :meth:`pandas.core.groupby.GroupBy.cumprod`, :meth:`pandas.core.groupby.GroupBy.cummin` and :meth:`pandas.core.groupby.GroupBy.cummax` with categorical column having absent categories, would return incorrect result or segfault (:issue:`16771`)
 - Bug in :meth:`pandas.core.groupby.GroupBy.nth` where NA values in the grouping would return incorrect results (:issue:`26011`)
+- Bug in :meth:`pandas.core.groupby.SeriesGroupBy.transform` where transforming an empty group would raise error (:issue:`26208`)
 
 
 Reshaping
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index ab60bdd8624fa..f4c53151bddc1 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -916,8 +916,12 @@ def transform(self, func, *args, **kwargs):
             s = klass(res, indexer)
             results.append(s)
 
-        from pandas.core.reshape.concat import concat
-        result = concat(results).sort_index()
+        # check for empty "results" to avoid concat ValueError
+        if results:
+            from pandas.core.reshape.concat import concat
+            result = concat(results).sort_index()
+        else:
+            result = Series()
 
         # we will only try to coerce the result type if
         # we have a numeric dtype, as these are *always* udfs
diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py
index 5edc2c13673bc..4c84c29ff98cb 100644
--- a/pandas/tests/groupby/test_grouping.py
+++ b/pandas/tests/groupby/test_grouping.py
@@ -552,6 +552,23 @@ def test_list_grouper_with_nat(self):
         expected = {pd.Timestamp('2011-01-01'): 365}
         tm.assert_dict_equal(result.groups, expected)
 
+    @pytest.mark.parametrize(
+        'func,expected',
+        [
+            ('transform', pd.Series(name=2, index=pd.RangeIndex(0, 0, 1))),
+            ('agg', pd.Series(name=2, index=pd.Float64Index([], name=1))),
+            ('apply', pd.Series(name=2, index=pd.Float64Index([], name=1))),
+        ])
+    def test_evaluate_with_empty_groups(self, func, expected):
+        # 26208
+        # test transform'ing empty groups
+        # (not testing other agg fns, because they return
+        # different index objects.
+        df = pd.DataFrame({1: [], 2: []})
+        g = df.groupby(1)
+        result = getattr(g[2], func)(lambda x: x)
+        assert_series_equal(result, expected)
+
 
 # get_group
 # --------------------------------