@@ -1255,6 +1255,27 @@ def test_response_transpose(
         assert x.shape == (T.size, sys.nstates)
 
 
+@pytest.mark.parametrize("ninputs", [1, 2])
+def test_forced_response_transpose_discrete(ninputs):
+    # Transposed inputs have time along the first axis, including when the
+    # time vector is computed from them (gh-931)
+    sys = ct.rss(3, 2, ninputs, dt=0.1)
+    U = np.random.default_rng(0).standard_normal((ninputs, 10))
+
+    resp = ct.forced_response(sys, U=U, squeeze=False)
+    resp_t = ct.forced_response(
+        sys, U=U.T, transpose=True, squeeze=False)
+    np.testing.assert_equal(resp_t.time, resp.time)
+    np.testing.assert_allclose(np.transpose(resp_t.outputs), resp.outputs)
+
+    # Scalar input with a given time vector
+    T = np.arange(10) * 0.1
+    resp = ct.forced_response(sys, T=T, U=1.0, squeeze=False)
+    resp_t = ct.forced_response(
+        sys, T=T, U=1.0, transpose=True, squeeze=False)
+    np.testing.assert_allclose(np.transpose(resp_t.outputs), resp.outputs)
+
+
 @pytest.mark.pandas
 def test_to_pandas():
     # Create a SISO time response
 
@@ -1112,7 +1112,8 @@ def forced_response(
                 raise ValueError('Parameters `T` and `U` can\'t both be '
                                  'zero for discrete-time simulation')
             # Set T to equally spaced samples with same length as U
-            if U.ndim == 1:
+            # (time is along the first axis of a transposed U)
+            if U.ndim == 1 or transpose:
                 n_steps = U.shape[0]
             else:
                 n_steps = U.shape[1]
@@ -1121,6 +1122,9 @@ def forced_response(
         else:
             if U.ndim == 0:
                 U = np.full((n_inputs, T.shape[0]), U)
+                if transpose:
+                    # U gets transposed back when its shape is checked
+                    U = U.T
     else:
         if T is None:
             raise ValueError('Parameter `T` is mandatory for continuous '