Registration of individual images remains a significant problem in the generation of accurate images collected using coherent imaging systems. An investigation of the performance of eight distinct image registration algorithms was conducted using data collected from a coherent optical imaging system developed by the Air Force Research Laboratories, Sensors Division, ARFL/SNJT. A total of 400 images of three distinct scenes were collected by SRJT and made available to the Air Force Institute of Technology (AFIT) for this study. Scenery was collected at 3 and 10 kilometers of wheeled vehicles supporting resolution and uniform target boards. The algorithms under study were developed by scientists and engineers at AFRL, and had varying levels of performance in terms of image mis-registration and execution time. These eight algorithms were implemented on a general-purpose computer running the MATLAB simulation environment. The algorithms compared included: block-match, cross-correlation, cross-search, directional-search, gradient-based, hierarchical-block, three-step, and vector-block methods. It was found that the cross-correlation, gradient-based and vector-block search techniques typically had the lowest error metric. The vector-block and cross-correlation methods proved to have the fastest execution times, while not suffering significant error degradation when estimating the registration shift of the test images.