A computational model of auditory localization resulting in performance similar to humans is reported. The model incorporates both the monaural and binaural cues available to a human for sound localization. Essential elements used in the simulation of the processes of auditory cue generation and encoding by the nervous system include measured head-related transfer functions (HRTFs), minimum audible field (MAF), and the Patterson-Holdsworth cochlear model. A two-layer feed-forward back-propagation artificial neural network (ANN) was trained to transform the localization cues to a two-dimensional map that gives the direction of the sound source. The model results were compared with (i) the localization performance of the human listener who provided the HRTFs for the model and (ii) the localization performance of a group of 19 other human listeners. The localization accuracy and front-back confusion error rates exhibited by the model were similar to both the single listener and the group results. This suggests that the simulation of the cue generation and extraction processes as well as the model parameters were reasonable approximations to the overall biological processes. The amplitude resolution of the monaural spectral cues was varied and the influence on the model's performance was determined. The model with 128 cochlear channels required an amplitude resolution of approximately 20 discrete levels for encoding the spectral cue to deliver similar localization performance to the group of human listeners.