An algorithm for real-time minefield detection from monochromatic airborne imagery must analyze the shape and spatial inter-relationship between compact, several pixel wide, regions that contrast with background. The regions are sparsely distributed over large areas and input data rates are very high. A hierarchical algorithm is described which meets these severe requirements. In progressing from lower to higher levels, computational operations become more complex but the volume of data to be analyzed decreases. At the lowest level of hierarchy, nonsuspect regions are rejected to drastically reduce the data rate. At increasingly higher levels, suspect regions are segmented into homogeneous subregions, morphological features of the subregions are extracted and subregions are classified based on extracted features. At the top level, spatial relationships between 'minelike' regions are determined and are used by statistical and knowledge-based methods to classify the imaged area as being a minefield with an estimated likelihood. An expert system performs syntactic pattern recognition, coordinates the algorithm, and integrates external information. The algorithm is being implemented on a distributed computing system consisting of workstation, vector processors, and transputers. Processor requirements, data rates, estimated probability of detection and false alarm rates and expected system performance are discussed.