The purpose of this study is to introduce a new method of evaluating human comprehension in the context of machine translation using a language translation program known as the FALCon (Forward Area Language Converter). The FALCon works by converting documents into digital images via scanner, and then converting those images to electronic text by use of the Optical Character Recognition (OCR) (ARL, 2004). Foreign text is then converted to English using the Machine Translator (MT). In all, the FALCon can negotiate 61 languages, though some languages do not have OCR capacity and the quality of translation varies between languages. Semitic languages such as Arabic, tend to be the most challenging for machine language translators. The participants for this experiment included 48 freshmen from the United States Military Academy enrolled in the General Psychology course, PL100. The results of this study have brought a few key points for consideration in Arabic machine translation. First, human understanding is not a factor to be ignored in gauging the usefulness of such translators. Secondly, the type of translation used can depend on the type of information needed, whether it is key people and places or the general plans or opinions. (Contains 6 figures.)