The inclusion of prior probabilities derived from the frequency histograms of the training sets has already been demonstrated to significantly improve the performance of a maximum likelihood classifier. Based on the same principles, a method is presently proposed to integrate the information of ancillary data layers (morphology, pedology, etc.) into the classification process. The statistical basis of this probabilistic approach is first described. A case study is then illustrated concerning a rugged area in Tuscany, (central Italy) sensed by bitemporal Landsat thematic mapper (TM) scenes. Ground references of nine cover categories were collected and digitized together with four ancillary data layers (elevation, slope, aspect, and soils). A maximum likelihood classification with nonparametric priors based only on the TM scenes was first tested, yielding a Kappa accuracy of 0.744. The ancillary data were integrated into the modified classifier, with notable increases in classification accuracy (up to Kappa equals 0.910). It is concluded that the utility of such an approach must be evaluated in relation to the characteristics of the landscape and the satellite imagery considered.