The Hotelling trace criterion (HTC) is useful for feature extraction so that multiclasses of statistical images can be separated by maximizing the between-class differences while minimizing the within-class variations. Optical implementation of the HTC has been successful by utilizing computer-generated spatial filters and a coded-phase processor. A simplified method of calculating the HTC discriminant functions from large-dimensional images by a small computer is also described. This method is useful when the within-class variation can be approximated by a covariance matrix of low rank.