Human photo-interpreters use expert knowledge and contextual information to help them analyze a scene. We have experimented with the Lockheed Expert System (LES) to see if contextual information can be useful in interpreting aerial photographs. First, the grey-scale image is segmented into uniform or slowly-varying intensity regions or contiguous textured regions using an edge-based segmentation technique. Next, the system computes a set of attributes for each region. Some of these attributes are based on local properties of that region only (e.g., area, average-intensity, texture-strength, etc.), while others are based on contextual or global information (e.g., adjacent-regions and nearby-regions). Finally, LES is given the task of classifying all the regions using the attribute values. It makes use of multiple goals and multiple rule sets to determine the best classification; regions, which do not satisfy any of the rules, are left unclassified. Unlike programs which use statistical methods, LES uses contextual information such as the fact that cars are likely to be adjacent to roads, which significantly improves its performance on regions which are difficult to classify.