The efficacy of using GIS technologies in the analysis of spatial patterns of disease and in the examination of etiologic hypotheses has been well demonstrated. However, several factors inhibiting such studies include the complex nature of the spatial patterns of disease; the multivariate nature of the natural and artificial environmental factors; pertinent data that is often erroneous, incomplete, or unreliable; and the vast amounts of spatial and temporal data that must be considered. One approach to addressing such problems is to supplement GIS technology with artificial neural networks (ANNs). ANNs are well suited for handling data that is erroneous or incomplete, handling/storing large amounts of information, recognizing complex spatial patterns, and addressing temporal issues.