diff --git a/GPy/core/svgp.py b/GPy/core/svgp.py
index 06a9749c..b8df625e 100644
--- a/GPy/core/svgp.py
+++ b/GPy/core/svgp.py
@@ -9,7 +9,7 @@ from ..inference.latent_function_inference import SVGP as svgp_inf
 
 
 class SVGP(SparseGP):
-    def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, name='SVGP', Y_metadata=None, batchsize=None):
+    def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, name='SVGP', Y_metadata=None, batchsize=None, num_latent_functions=None):
         """
         Stochastic Variational GP.
 
@@ -41,8 +41,12 @@ class SVGP(SparseGP):
         SparseGP.__init__(self, X_batch, Y_batch, Z, kernel, likelihood, mean_function=mean_function, inference_method=inf_method,
                  name=name, Y_metadata=Y_metadata, normalizer=False)
 
-        self.m = Param('q_u_mean', np.zeros((self.num_inducing, Y.shape[1])))
-        chol = choleskies.triang_to_flat(np.tile(np.eye(self.num_inducing)[:,:,None], (1,1,Y.shape[1])))
+        #assume the number of latent functions is one per col of Y unless specified
+        if num_latent_functions is None:
+            num_latent_functions = Y.shape[1]
+
+        self.m = Param('q_u_mean', np.zeros((self.num_inducing, num_latent_functions)))
+        chol = choleskies.triang_to_flat(np.tile(np.eye(self.num_inducing)[:,:,None], (1,1,num_latent_functions)))
         self.chol = Param('q_u_chol', chol)
         self.link_parameter(self.chol)
         self.link_parameter(self.m)