Composite classifiers that are constructed by combining a number of component classifiers are designed and evaluated on the problem of automatic target recognition (ATR) using forward-looking iR (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers. A number of classifier fusion algorithms, which combine the outputs of all the component classifiers, and classifier selection algorithms, which use a cascade architecture that relies on a subset of the component classifiers, are analyzed. Each composite classifier is implemented and tested on a large data set of real FLIR images. The performance of the proposed composite classifiers are compared based on their classification ability and computational complexity. it is demonstrated that the composite classifier based on a cascade architecture greatly reduces computational complexity with a statistically insignificant decrease in performance in comparison to standard classifier fusion algorithms.