Cytochrome P450 aromatase is a key steroidogenic enzyme that converts androgens to estrogens in vertebrates. There is much interest in aromatase inhibitors (AIs) both because of their use as pharmaceuticals in the treatment of estrogen-sensitive breast cancers, and because a number of environmental contaminants can act as AIs, thereby disrupting endocrine function in humans and wildlife through suppression of circulating estrogen levels. The goal of the current work was to develop a mechanism-based structure-activity relationship (SAR) categorization framework highlighting the most important chemical structural features responsible for inhibition of aromatase activity. Two main interaction mechanisms were discerned: steroidal and non-steroidal. The steroid scaffold is most prominent when the structure of the target chemical is similar to the natural substrates of aromatase - androstenedione and testosterone. Chemicals acting by non-steroidal mechanism(s) possess a heteroatom (N, O, S) able to coordinate the heme iron of the cytochrome P450, and thus interfere with steroid hydroxylation. The specific structural boundaries controlling AI for both analyzed mechanisms were defined, and a software tool was developed that allowed a decision tree (profile) to be built discriminating AIs by mechanism and potency. An input chemical follows a profiling path and the structure is examined at each step to decide whether it conforms with the structural boundaries implemented in the decision tree node. Such a system would aid drug discovery efforts, as well as provide a screening tool to detect environmental contaminants that could act as AIs.