From the Automatic Language Processing Advisory Committee (ALP AC) (Pierce et al., 1966) machine translation (MT) evaluations of the ‘60s to the Defense Advanced Research Projects Agency (DARPA) Global Autonomous Language Exploitation (GALE) (Olive, 2008) and National Institute of Standards and Technology (NIST) (NIST, 2008) MT evaluations of today, the U.S. Government has been instrumental in establishing measurements and baselines for the state-of-the-art in MT engines. In the same vein, the Automated Machine Translation Improvement Through Post-Editing Techniques (PEMT) project sought to establish a baseline of MT engines based on the perceptions of potential users. In contrast to these previous evaluations, the PEMT project’s experiments also determined the minimal quality level output needed to achieve before users found the output acceptable. Based on these findings, the PEMT team investigated using post-editing techniques to achieve this level. This paper will present experiments in which analysts and translators were asked to evaluate MT output processed with varying post-editing techniques. The results show at what level the analysts and translators find MT useful and are willing to work with it. We also establish a ranking of the types of post-edits necessary to elevate MT output to the minimal acceptance level.