MODERN MANUFACTURING TASKS ARE MAKING INCREASING USE OF MACHINE VISION. UNDERLYING MANY OF THE MACHINE VISION ACTIVITIES IS THE NEED FOR ACCURATE DETERMINATION OF THE 3-D OBJECT LOCATION. NUMEROUS METHODS EXIST FOR OBTAINING OBJECT LOCATION BUT STEREO VISION IS ONE OF THE BEST UNDERSTOOD METHODS OF OBTAINING THE REQUIRED 3-D DATA. HOWEVER, STEREO VISION IS PLAGUED BY TWO CONTINUING DIFFICULTIES; DETERMINING THE CORRESPONDENCE OF THE SAME FEATURE IN EACH IMAGE AND THE COMPUTATIONAL COMPLEXITY OF THE MATCHING PROCESS. THIS PAPER PRESENTS A CONSTELLATION MATCHING ALGORITHM (CMORPH ALGORITHM) THAT CAN PROVIDE AN ACCEPTABLE COMPUTATIONAL SOLUTION TO THESE TWO DIFFICULTIES. AS SUCH, THE ALGORITHM FINDS THE BEST NEAR-OPTIMAL MATCH BETWEEN TWO SETS (CONSTELLATIONS) OF ATTRIBUTED POINT FEATURES THAT HAVE BEEN EXTRACTED FROM STEREO IMAGE PAIRS USING COMMON METHODS. THE MATCHING IS FORMULATED AS A