Correlation-based translated-feature finding techniques are fast and effective in identifying targets in test images despite unknown translation. Information involving both translation and in-plane orientation of targets, however, is important in many industrial machine vision applications such as manufacturing and quality assurance. A traditional correlation based technique that expands the search criteria to include in-plane orientation is based upon use of a bank of filters that each implement a feature finding operation for one rotation of the target. This computational complexity of this approach is inversely proportional to the resolution of the orientation estimate. This thesis develops a correlation based method for translation and in-plane orientation feature finding that requires only two underlying correlation filter operations. A composite filter is constructed from a specially arranged and complex weighted sum of the set of the translated exemplar filters contained the usual filter bank. The arrangement allows for robust peak location detection yielding the target position and the multiplier angle that is extracted from the amplitude of the peak output response supplys an orientation estimate. A demonstration system using two such filters in an iterative fashion to counteract different sources of interference produced results accurate to plus or minus 1 degree 100 times faster than the traditional system.