Non-point source pollution is difficult to monitor and manage because contaminants are generated from a wide range of human activities dispersed across extensive areas and the relative influence of major sources such as agriculture and urban runoff can be challenging to distinguish from each other. In this study, a self-organizing map (SOM) analysis was used to evaluate the contributions of various forms of land cover to water quality impairment in southern Ontario, a population-dense, yet highly agricultural region where urban expansion and agricultural intensification have been associated with continued water quality impairment. Watersheds were classified into eight spatial clusters, representing four categories of agriculture, one urban, one natural, and two mixed land use clusters. All four agricultural clusters had high nitrate-N concentrations, but levels were especially high in watersheds with extensive corn and soybean cultivation, where exceedances of the 3 mg L-1 water quality objective dramatically increased above a threshold of ‍‌~30% row crop cover. Maximum P concentrations also occurred in the most heavily tile-drained cash crop watersheds, but associations between P and landuse were not as clear as for N. Expansions in tile-drained corn and soybean area, often at the expense of mixed, lower intensity agriculture are not unique to this area and suggest that river NO3-N levels will continue to increase in the future. The SOM approach provides a powerful means of simplifying heterogeneous land cover patterns that can be associated with water quality impairment and identify problem areas for management.