Information on the spatial pattern of African animal trypanosomosis forms a prerequisite for rational disease management, but few data exist for any country in the continent. The present study describes a raster or grid-based Geographic Information System for Togo, a country representative of subhumid West Africa, with data layers on tsetse, trypanosomosis, animal production, agriculture and land use. The paper shows how trypanosomosis prevalence and packed cell volume (PCV) map displays may be predicted from correlations between representative field data and environmental and satellite data acquired from the National Oceanographic and Atmospheric Administration (NOAA) and Meteosat platforms. Discriminant analytical methods were used to assess the relationship between the amount of field data used and the accuracy of the predictions obtained. The accuracy of satellite derived predictions decreases from tsetse abundance to trypanosomosis prevalence to PCV value. The predictions improve when eco-climatic and epidemiological predictors are combined. In Togo, and probably elsewhere, the patterns of trypanosomosis prevalence and PCV are much influenced by animal husbandry and other anthropogenic factors. Additional predictor variables, incorporating these influences might therefore further improve the models.