GIS data may include optical and radar imagery, and categorical information such as soils maps and land planning strategies, all of which can assist thematic mapping. We now have several decades of experience with thematic mapping from spectral data alone. We also have experience with the analysis of radar imagery, while hyperspectral thematic mapping techniques are now also becoming feasible. However, successful machine-assisted analysis of mixed optical and radar data is not straightforward, and is complicated further when categorical data is also involved. Often simplistic methods involving stacked vectors of all the available data are used, but the incommensurate data types means that a single analytical procedure, even if acceptable, will often yield poor results. Methods commonly used for mixed image data analysis are reviewed and a set of desirable criteria for an operational method for thematic mapping from disparate data types are presented. Finally we propose a fusion strategy based on (i) analysing each data type with procedures most suited to its particular characteristics and (ii) fusing at the class level, involving combination rules that work with labels rather than measurement vectors. The method is proposed as suited to GIS analysis, particularly when the data sets are distributed and thus accessed over a network.