We present an algorithm based on the self-organizing map (SOM) which models multi-sensory integration as realized by the superior colliculus (SC). Our algorithm differs from other algorithms for multi-sensory integration in that it learns mappings between modalities' coordinate systems, it learns their respective reliabilities for different points in space, and uses mappings and reliabilities to perform cue integration. It does this in only one learning phase without supervision and such that calculations and data structures are local to individual neurons. Our simulations indicate that our algorithm can learn near-optimal integration of input from noisy sensory modalities.