We propose a class-associative correlation filter based technique for detecting a class of objects consisting of dissimilar patterns. The fringe-adjusted joint transform correlation algorithm is utilized to enhance the correlation performance, thus ensuring a strong and equal correlation peak for each element of the selected class. For enhanced performance, an enhanced version of the fringe-adjusted filter is incorporated in the class-associative multiple target detection process. The feasibility of the proposed technique has been tested by computer simulation.