Lesion to the posterior parietal cortex in monkeys and humans produces spatial deficits in movement and perception. In recording experiments from area 7a, a cortical subdivision in the posterior parietal cortex in monkeys, we have found neurons whose responses are a function of both the retinal location of visual stimuli and the position of the eyes in the orbits. By combining these signals area 7 a neurons code the location of visual stimuli with respect to the head. However, these cells respond over only limited ranges of eye positions (eye-position-dependent coding). To code location in craniotopic space at all eye positions (eye-position-independent coding) an additional step in neural processing is required that uses information distributed across populations of area 7a neurons. We describe here a neural network model, based on back-propagation learning, that both demonstrates how spatial location could be derived from the population response of area 7a neurons and accurately accounts for the observed response properties of these neurons.