The goal of our research is to develop an effective and efficient clutter rejector with the use of an eigenspace transformation and a multilayer perceptron (MLP) that can be incorporated into an automatic target recognition system. An eigenspace transformation is used for feature extraction and dimensionality reduction. The transformations considered in this research are principal-component analysis (PCA) and the eigenspace separation transformation (EST). We fed the result of the eigenspace transformation to an MLP that predicts the identity of the input, which is either a target or clutter. Our proposed clutter rejector was tested on two huge and realistic datasets of second-generation forwardlooking infrared imagery for the Comanche helicopter. In general, both the PCA and EST methods performed satisfactorily with minor differences. The EST method performed slightly better when a smaller amount of transformed data was fed to the MLP, or when the positive and negative EST eigentargets were used together.