By conventional spike count measures, auditory neurons in the cat's anterior ectosylvian sulcus cortical area are broadly tuned for the location of a sound source. Nevertheless, an artificial neural network was trained to classify the temporal spike patterns of single neurons according to sound location. The spike patterns of 73 percent of single neurons coded sound location with more than twice the chance level of accuracy, and spike patterns consistently carried more information than spike counts alone. In contrast to neurons that are sharply tuned for location, these neurons appear to encode sound locations throughout 360 degrees of azimuth.