diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 931d18dc349f3..27d78d7c313c4 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -629,6 +629,7 @@ Groupby/resample/rolling
 - Bug in :meth:`Rolling.var` and :meth:`Rolling.std` would give non-zero result with window of same values (:issue:`42064`)
 - Bug in :meth:`.Rolling.var` would segfault calculating weighted variance when window size was larger than data size (:issue:`46760`)
 - Bug in :meth:`Grouper.__repr__` where ``dropna`` was not included. Now it is (:issue:`46754`)
+- Bug in :meth:`DataFrame.rolling` gives ValueError when center=True, axis=1 and win_type is specified (:issue:`46135`)
 
 Reshaping
 ^^^^^^^^^
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index 5006db44849e0..9617e27cc2e4b 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -1129,12 +1129,8 @@ def _center_window(self, result: np.ndarray, offset: int) -> np.ndarray:
         """
         Center the result in the window for weighted rolling aggregations.
         """
-        if self.axis > result.ndim - 1:
-            raise ValueError("Requested axis is larger then no. of argument dimensions")
-
         if offset > 0:
-            lead_indexer = [slice(None)] * result.ndim
-            lead_indexer[self.axis] = slice(offset, None)
+            lead_indexer = [slice(offset, None)]
             result = np.copy(result[tuple(lead_indexer)])
         return result
 
diff --git a/pandas/tests/window/test_win_type.py b/pandas/tests/window/test_win_type.py
index 11e288591fe75..8c8e9cadbfdc1 100644
--- a/pandas/tests/window/test_win_type.py
+++ b/pandas/tests/window/test_win_type.py
@@ -686,3 +686,18 @@ def test_weighted_var_big_window_no_segfault(win_types, center):
     expected = Series(np.NaN)
 
     tm.assert_series_equal(result, expected)
+
+
+@td.skip_if_no_scipy
+def test_rolling_center_axis_1():
+    df = DataFrame(
+        {"a": [1, 1, 0, 0, 0, 1], "b": [1, 0, 0, 1, 0, 0], "c": [1, 0, 0, 1, 0, 1]}
+    )
+
+    result = df.rolling(window=3, axis=1, win_type="boxcar", center=True).sum()
+
+    expected = DataFrame(
+        {"a": [np.nan] * 6, "b": [3.0, 1.0, 0.0, 2.0, 0.0, 2.0], "c": [np.nan] * 6}
+    )
+
+    tm.assert_frame_equal(result, expected, check_dtype=True)