We show that by first measuring the line integrals of a two-dimensional picture f(x, y) via the Radon transform, certain feature-extraction operations useful in pattern recognition may be computed very easily; that is, the computational overhead for these feature-extraction operations is much less in the Radon space than in the direct space. In particular, we consider the following features: (1) moments of f(x, y) invariant to translation, rotation, geometric scaling, and linear contrast scaling; (2) two geometric features, polar projections and convex hull, that have similar invariance properties; (3) the Fourier power spectrum of f(x, y) integrated along radial piewedge and annular bins in the Fourier space; and (4) the Hough transform for detection of straight edges. Much of the motivation for this work lies in its implementation as an optical pattern recognition system. We show an optical-digital hardware implementation for the rapid computation of both the Radon transform and the feature extractions. By "rapid," we mean that the transform and feature-extrac-tion operations on a 512 X 512 image may be accomplished at video rates (1 /30 s per image). We point out advantages of this system over more traditional optical pattern recognition schemes that rely on the use of Fourier optics. Some experimental results are shown.