Human-caused forest fires are a serious problem throughout the world. Believing that there are predictable characteristics common to all fires, we analyzed the historical human-caused fire occurrence data for the Whitecourt Provincial Forest of Alberta using artificial neural network and geographic information system (ARC/INFO) technology. These data were also analyzed using logistic regression analysis (the binary legit model), which served as the 'domain expert' to identify the im portant input variables. A 314 fire and no-fire data set for the period 1986-1990 was used for training. The observations were whether at least one fire occurred, on a certain day, in one of the eight geographic zones defined within the study area. The models developed were tested using data from the 1991-1992 fire seasons, which had 58 fire obser- vations. Using as input variables the Canadian Fire Weather Index for the day, area in km2 of the geographic zone, and district (a 0/1 dummy vari- able from the logistic regression model, which accounts for observa- tions within a forest district where human use is higher), the resultant model had four input nodes and two output nodes, and correctly predicted 85 percent of the no-fire observations and 78 percent of the fire obsenmtions.