During the work by many scientists over the last 50 years, we have solved many of the problems that seemed to be holding the field back: System layout, lenses specialized for this purpose, suitable detector arrays, SLMs for input and for filtering, designs for filters, online evolution of filters, and so forth. Yet it is hard to find this technology in use anywhere. Something is clearly wrong, and we know one major problem that has never been solved and, in fact, is seldom acknowledged in print: Only very simple problems are linearly discriminable, so they cannot be discriminated well by a linear discriminant. But, the wonderful property of correlating all parts of the input pattern in parallel is only possible by Fourier methods. We see no way whatever for overcoming the contradictory goals of space or time invariant operations and need of implementing the most powerful nonlinear discriminants (clearly this can only be done with nonlinear discriminants). Those two contradictory goals seem to condemn Fourier filtering (electronic or optical) to being interesting but not very helpful. As this is a paper on a new kind of pattern recognition and it is not limited to Fourier optics, it is important to provide some useful concepts in Pattern Recognition. Our discussion is simple and brief. It has no proofs but it does offer the reader some of the concepts that help in understanding the new aspects of the material presented here. This work will not propose or develop improvements on any of the configurations and components. This is a study of the way Fourier filtering can have it both ways. Prior analyses are still valid, but, at a higher level, we can have it both ways. This review is aimed at allowing both the space or time invariant filtering while implementing an extremely powerful linear discriminants. Aspects of this have been published but in a regrettably short form and with very little of the background on pattern recognition. Here, we begin with a brief introduction to pattern recognition as will be needed to make this review finite. After the way to achieve powerful target discrimination and target location is shown, I describe how to apply it to optical Fourier filtering for pattern recognition. Some situations seem more likely to use Optical Fourier pattern recognition than others. We explore some of those.