The view based approach to object recognition relies upon the co-activation of 2-D pictorial elements or features. This approach is limited to generalising recognition across transformations of objects in which considerable physical similarity is present in the stored 2-D images to which the object is being compared. It is, therefore, unclear how completely novel views of objects might correctly be assigned to known views of an object so as to allow correct recognition from any viewpoint. The answer to this problem may lie in the fact that in the real world we are presented with a further cue as to how we should associate these images, namely that we tend to view objects over extended periods of time. In this paper, neural network and human psychophysics data on face recognition are presented which support the notion that recognition learning can be affected by the order in which images appear, as well as their spatial similarity.