We review the use of a neural network model of a cerebral cortical area as an aid to understanding these areas function. The basic model is a feedforward multi-layer network that learns to transform the coordinates of a visual stimulus from a retinocentric to a craniocentric reference frame using backpropagation. The similarity of ceratin features acquired by the model's components with the response properties of neurons in the posterior parietal cortex made the model a candidate for studying the cortical area's processing in an artificial system. An extension of the model to one the transformed retinal coordinates into body-centered ones predicted response properties that were later confirmed by neurophysiological experiments. Simulation of electrical stimulation of the model predicted a pattern of effects similar to the one later obtained by stimulation of the model predicted a pattern of effects similar to the one later obtained by stimulation of a specific region of the parietal cortex. More importantly, study of the response properties of the model's units provided a simple explanation of how the parietal cortex might compute coordinate transformations and of why certain manipulations such as stimulation should produce the effects observed. The same algorithm for coordinate transformation was also obtained in an analogous network trained with a learning rule biologically more plausible than backpropagation. These results show that neural network modeling is a useful adjunct to the neurophysiological and psychophysical techniques we are using to study the function of the posterior parietal cortex.