A new method for assessing the performance of popular image matching algorithms is presented. Specifically, the method assesses the type of images under which each of the algorithms reviewed herein perform to its maximum or highest efficiency. The efficiency is measured in terms of the number of matches founds by the algorithm and the number of type I and type II errors encountered when the algorithm is tested against a specific pair of images. Current comparative studies asses the performance of the algorithms based on the results obtained in different criteria such as speed, sensitivity, occlusion, and others. These studies are an important resource to understand the behavior of the algorithms and their influence on the results obtained. But they do not account for the inherent characteristics of the algorithms that derive the process through which the matching features are evaluated, filtered, and finally selected. Moreover, these methods cannot be used to predict the efficiency or level of accuracy that could be reached by using one algorithm or the other depending on of the type of images. This ability to predict performance becomes handy in situations where time is a limiting factor in a project because it allows one to quickly predict which algorithm will save the most time and resources.