diff --git a/GPy/kern/_src/kern.py b/GPy/kern/_src/kern.py
index 03714e62..6daff739 100644
--- a/GPy/kern/_src/kern.py
+++ b/GPy/kern/_src/kern.py
@@ -25,7 +25,7 @@ class Kern(Parameterized):
 
             is the number of dimensions to work on. Make sure to give the 
             tight dimensionality of inputs.
-            You moset likely want this to be the integer telling the number of 
+            You most likely want this to be the integer telling the number of 
             input dimensions of the kernel.
             If this is not an integer (!) we will work on the whole input matrix X,
             and not check whether dimensions match or not (!).
@@ -44,7 +44,7 @@ class Kern(Parameterized):
         super(Kern, self).__init__(name=name, *a, **kw)
         try:
             self.input_dim = int(input_dim)
-            self.active_dims = active_dims if active_dims is not None else slice(0, input_dim, 1)
+            self.active_dims = active_dims# if active_dims is not None else slice(0, input_dim, 1)
         except TypeError:
             # input_dim is something else then an integer
             self.input_dim = input_dim
@@ -231,7 +231,9 @@ class CombinationKernel(Kern):
     def get_input_dim_active_dims(self, kernels, extra_dims = None):
         #active_dims = reduce(np.union1d, (np.r_[x.active_dims] for x in kernels), np.array([], dtype=int))
         #active_dims = np.array(np.concatenate((active_dims, extra_dims if extra_dims is not None else [])), dtype=int)
-        input_dim = [k.input_dim for k in kernels]
+        input_dim = np.array([k.input_dim for k in kernels])
+        if np.all(input_dim[0]==input_dim):
+            input_dim = input_dim[0]
         active_dims = None
         return input_dim, active_dims
 
diff --git a/GPy/kern/_src/stationary.py b/GPy/kern/_src/stationary.py
index 37acbf2d..a560f8ad 100644
--- a/GPy/kern/_src/stationary.py
+++ b/GPy/kern/_src/stationary.py
@@ -139,7 +139,7 @@ class Stationary(Kern):
             #self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0)/self.lengthscale**3
             tmp = dL_dr*self._inv_dist(X, X2)
             if X2 is None: X2 = X
-            self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(self._slice_X(X)[:,q:q+1] - self._slice_X(X2)[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in xrange(self.input_dim)])
+            self.lengthscale.gradient = np.array([np.einsum('ij,ij,...', tmp, np.square(X[:,q:q+1] - X2[:,q:q+1].T), -1./self.lengthscale[q]**3) for q in xrange(self.input_dim)])
         else:
             r = self._scaled_dist(X, X2)
             self.lengthscale.gradient = -np.sum(dL_dr*r)/self.lengthscale
diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py
index 65998ad2..4a0cb076 100644
--- a/GPy/testing/kernel_tests.py
+++ b/GPy/testing/kernel_tests.py
@@ -314,13 +314,14 @@ class KernelTestsMiscellaneous(unittest.TestCase):
         self.sumkern.randomize()
 
     def test_active_dims(self):
-        #self.assertEqual(self.sumkern.input_dim, 10)
-        #self.assertEqual(list(self.sumkern.active_dims), [0,1,2,3,7,9])
         # test the automatic dim detection expression for slices:
         start, stop = 0, 277
         for i in range(start,stop,7):
             for j in range(1,4):
                 GPy.kern.Kern(int(np.round((i+1)/j)), slice(0, i+1, j), "testkern")
+        # test the ability to have only one dim
+        sk = GPy.kern.RBF(2) + GPy.kern.Matern32(2)
+        self.assertEqual(sk.input_dim, 2)
 
     def test_which_parts(self):
         self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=[self.linear, self.matern]), self.linear.K(self.X)+self.matern.K(self.X)))