A practical method for the recognition of armored vehicles in FLIR (forward-looking infrared radar) imagery is presented. This unique method is applied to second-generation IR images of unoccluded vehicles. The principal value of our technique is that recognition is invariant to large changes (up to 45 degrees) in target depth aspect (rotation into or out of the view plane), as well as large changes in range from sensor to target. Feature detection is robust, and is not easily fooled by various irregularities, appendages, and variations among the test targets. This is accomplished by ensuring that certain geometric relationship constraints are satisfied. Various length measurements from target centroid and perimeter are combined into six ratios to supply input features. Such simple, reliable measurements provide low computational complexity, thus having the potential to scale well to larger target sets. Real-time implementation may also be possible. The principal limitation is that, thus far, we have tested the system on less than a dozen different vehicle types. Nevertheless, discrimination between similar-looking vehicles was very high. Also the algorithm works on armored vehicles tha have markedly different appearances (e.g., with or without turrets). Currently our method is reliable with up to 10% shot-noise appearing in the scene. Pattern classification is achieved using an expert system, but this has not been completely automated. We are now using the CLIPS expert system shell, and heuristically coding the various allowable feature ranges. EMYCIN type confidence factors are also being used to give a quantitative measure of classification.