Distortion-invariant correlation filters are used to detect and recognition distorted objects in scenes. They are used in a correlator and are thus shift-invariant. We describe a new way to design distortion-invariant correlation filters that ensures good generalization (same performance on training and test sets) and improved capacity (fewer filters that recognize distorted versions of multiple classes of objects). The traditional way of designing correlation filters uses different types of frequency domain preprocessing and linear combination of training images. We show that these different approaches can be implemented in a framework using linear combination of eigen-images of preprocessed training data. Using eigen-domain data is shown to produce filters that generalize better and have large capacity. We show results on SAR data with multiple classes of objects using eigen-MINACE filters.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.