A proof-of-concept system has been designed to detect airplanes in aerial imagery using their general shape. The system has three distinct levels of processing: 1) a low level that extracts line segments from raw image data, 2) a middle level that combines the line segments into geometric feature primitives, and 3) a top level that checks for positional coincidences of features to perform the airplane detections. The system has been tested on a limited set of images with encouraging results. More extensive testing of an enhanced version of the system is imminent. The initial testing seems to show that geometric features can be efficiently used to detect airplanes with low false alarm rates. The detection process is relatively insensitive to view angle, object orientation, and sun angle. No a priori scene knowledge is used. Furthermore, low contrast conditions and limited occlusion can be tolerated. The airplane description provided to the system to drive the detection process is a very general one, suitable for a broad range of look-down views. The speed performance of the system is also good. A low level line segment extraction algorithm has been invented to greatly improve the speed and reliability of the initial image processing steps compared with similar older methods. The middle level of the system has also been designed to give efficient performance and relatively fast execution speeds. The overall speed results are sufficiently good to suggest the future possibility of near frame speed performance, given a suitable custom hardware architecture.