A am having some trouble when pickling numpy arrays. Basically I use one python script to create the array and then pickle it. When I load the pickled array using a different python script it appears to load fine. When I try to perform a matrix multiply on the array with a vector (using dot) python crashes with a segmentation violation. The array contains 64bit floating point numbers and has a shape of (47, 16384) and the vector has a shape of (16384,1). Other shapes I have tested also have this problem. A workaround is to create a new array that is initialized with the data from the first. For example: a = array(pickle.loads(buffer)) After calling this command the script runs fine. Here is some information on my system setup: OS: Macos X Leopard 64bit Intel numpy.version.version = 1.0.5.dev4624 Below is a simple script that reproduces this error on my system. I would like to get this problem fixed so that I don't have to use this work around every time I load a pickled array. Is this a known bug? I could not find it in the bug tracker. Thanks in advance for the help. Dave import pickle from numpy import array,dot,ones,random # Create the arrays a = random.normal(size=(47,128*128)) b = ones((128*128,2)) c = ones((128*128,1)) # Test dot print "Computing dot product on original arrays..." print dot(a,b).shape # WORKS print dot(a,c).shape # WORKS print "Pickle and unpickel the array with workaround." buffer = pickle.dumps(a) a = array(pickle.loads(buffer)) # Test dot print "Computing dot product with workaround..." print dot(a,b).shape # WORKS print dot(a,c).shape # WORKS print "Pickle and unpickel the array without workaround." a = pickle.loads(buffer) # Test dot print "Computing dot product without workaround..." print dot(a,b).shape # WORKS print dot(a,c).shape # CRASH