Two digital image registration algorithms are tested and evaluated on a personal computer system. The two algorithms, correlation and sequential similarity detection, are tested and evaluated for speed of execution, accuracy, and optimum parameter determination. The programs are written in BASIC and can be easily converted to FORTRAN or other high-level language. Three different polynomial functions are tested and evaluated to improve the resolution of the correlation determination. Based on the results of the tests, it was concluded that the testing of registration algorithms is feasible in small computer systems and that sequential similarity detection is faster. An optimum threshold setting can be determined for an individual image. Increasing the magnitude of the SSDA threshold parameter increases the execution time of the SSDA program. Also, the resolution of the correlation can be improved with a curve fitting technique. / Master of Engineering