The strength of GIS is in providing a rich data infrastructure for combining disparate data in meaningful ways by using a spatial arrangement (e.g., proximity). As a toolbox, a GIS allows planners to perform spatial analysis using geo-processing functions such as map overlay, connectivity measurements or thematic map coloring. Although, this makes effective the geographic visualization of individual variables, complex multi-variate dependencies are easily overlooked. The required step to take GIS beyond a tool for automating cartography is to incorporate the ability of analyzing and condensing a large number of geo-referenced variables into a single forecast or score. This is where data mining promises great potential benefits and the reason why there is such a hand-in-glove fit between GIS and data mining. INGENS (INductive GEographic iNformation System) is a prototype GIS which integrates data mining tools to assist users in their task of topographic map interpretation. The spatial data mining process is aimed at a user who controls the parameters of the process by means of a query written in a mining query language. In this talk, I present SDMQL (Spatial Data Mining Query Language), a spatial data mining query language used in INGENS. Currently, SDMQL supports two data mining tasks: inducing classification rules and discovering association rules. For both tasks the language permits the specification of the task-relevant data, the kind of knowledge to be mined, the background knowledge and the hierarchies, the interestingness measures and the visualization for discovered patterns. Some constraints on the query language are identified by the particular mining task. I describe the syntax of the query language and finally I briefly illustrate the application to a real repository of maps.