In this paper, we investigate several fusion techniques for designing a composite classifier to improve the performance (probability of correct classification) of FLIR ATR. In this research, we propose to use four ATR algorithms for fusion. The individual performance of the four contributing algorithms ranges from 73.5% to about 77% of probability of correct classification on the testing set. We propose to use Bayes classifier, committee of experts, stacked-generalization, winner-takes-all, and ranking-based fusion techniques for designing the composite classifiers. The experimental results show an improvement of more than 6.5% over the best individual performance.