This paper describes the results of using video feedback to support the calculation of the radon transform and angular correlation. Application of this type of optical processor to the extraction of features from synthetic aperture radar imagery of ships is described. By theoretical analysis and experimental evaluation of the optical architecture outputs (using some digital post processing) it is shown that the internal structure of objects, their primitive dimensions, and even their boundary can be obtained. In particular, the power of angular correlation to extract object length, width, area, aspect ratio, orientation and boundary from suitably thresholded images is shown. Alternative techniques to extract the object boundary based on angular correlation are discussed, including direct optical computation as well as digital deconvolution. Simple optical shapes, such as squares, rectangles, and triangles were used to initially compare the optical architecture outputs with digital baseline calculations. In addition, the test and evaluation of the optical processor using simple synthetic models of ship data is discussed. The results of a study that uses object primitives (derivable from angular correlation) in conjunction with the Radon transform (along the longitudinal axis) to classify ships using a backpropagation neural network also are described. A discussion of these results is presented pertaining to their broader application to optical parts inspection and to feature extraction from imaging infrared surveillance sensors. The practical implementation of such a processor in compact form using lenslet array optics also is discussed.