In the course of our research on sound localization we have measured direction‐dependent pinna transfer functions of eight observers. The magnitudes of these functions have peaks and valleys that vary in spectral locus across subject and across source location. We addressed the question of whether these spectral patterns contain sufficient information for accurate localization of sounds in space. We used standard multivariate statistical techniques to develop a model of sound localization based on pattern recognition principles. A Bayesian classification procedure is employed to make a decision as to the most probable source location. The errors made by this procedure are compared to the errors made by subjects when judging source location in free‐field listening conditions. Preliminary results are extremely encouraging. The source positions for which the model makes errors match quite well those for which the subjects make errors. [Work supported by NSF and NIH.]