The objective of this thesis is to examine the validity of functional near-infrared spectroscopy (fNIR) to examine brain regions involved in rule based (RB) and information integration (II) category learning. We predicted similar patterns of activation found by past studies that used fMRI scans. Our goal was to test if fNIR would be able to detect changes in blood oxygenation levels of participants who learned to categorize (learners) vs. those that did not (non learners). The stimulus set comprised of lines that differed in length and orientation. Participants had to learn to categorize by trial and error based on the feedback provided. Behavioral and neuroimaging data was recorded for both RB and II conditions. Results showed an upward trend in response accuracy over trials for participants identified as learners. Furthermore, blood oxygenation levels reported by fNIR indicated a systematic increase in oxygen consumption for learners as compared to non learners. These areas of increased prefrontal cortex activity recorded by fNIR correspond to the same areas found to be involved in categorization by fMRI. This paper reviews the background of category learning, explores various neuroimaging techniques in categorization research, and investigates the efficacy of fNIR as a relatively new neuroimaging modality by comparing it to fMRI.