Individual tree crowns were automatically located in aerial electro-optical sensor images of a forested scene in Ontario, Canada. The images have pixels with a ground dimension of 36 cm. A model of the appearance of individual tree crowns in a single channel of the image data was used to generate a set of templates. The model has parameters for size, shape, and projected one-sided leaf area density distribution, which are evaluated according to the expected range of values for the scene. Knowledge of the scene illumination and sensing geometry is also incorporated into the model. Tree crown locations were derived from the locations of plausible linear relationships between the templates and the sensed images, as determined with a weighted least-squares straight line regression analysis. The weighting was used to accommodate crown margin irregularity. The procedure successfully located 57% of the trees in a 548 member ground-surveyed sample representing a wide range of species, sizes, and growing situations. Success was defined by a one-to-one spatial relationship between computed crown extents and reference locations. The success rate increased to 73% when only 224 trees that had almost no physical contact with their neighbors were considered. The commission error rate was estimated to be 23%.