The threshold of toxicological concern (TTC) is a practical tool to screen and estimate safe human exposure levels to compounds lacking adequate data for safety evaluation. In the absence of compound-specific toxicological data, approaches, such as the Cramer Classification Scheme, can be utilized to estimate an appropriate exposure level. The Cramer Classification Scheme is a 33-question decision tree used to classify the potential toxicity of non-carcinogenic compounds with limited toxicity data based upon a curated dataset of oral NOAELs. Divided into 3 classes, the Cramer decision tree scheme categorizes organic chemicals as possessing either low (Class I), moderate (Class II), or high (Class III) potential for toxicity. Although it is possible to manually assess compounds using the decision tree, Toxtree software has developed an automated software approach which implements the Cramer decision tree scheme. Two other decision tree methods, Cramer rules with extensions and the revised Cramer decision tree, are also available and utilized by many scientists in order to predict probable toxicological risk of compounds, although standardized regulatory guidance regarding which tree is most appropriate is currently unavailable, especially considering current applications of the TTC to a variety of chemicals and potential human exposure routes and regimens. The Cramer rules with extensions includes 5 additional questions as derived by Munro et al. (1996) to overcome possible misclassification of compounds. By contrast, the revised Cramer decision tree is a shortened scheme which combines several Cramer questions and incorporates additional industry-derived data on metabolism, toxicity and biochemistry of compounds within the dataset. Recently, analysis of several compounds using these various Cramer schemes elicited conflicting data. Evaluation and comparative analysis among examples using the original, extended, and revised versions of the Cramer decision tree are presented in this poster. By presenting these chemical examples with conflicting Cramer classification, it is possible to gain a better understanding of potential risk associated with each compound and influence of classification scheme on prediction outcomes. Analysis of these chemical examples assists in determination of the most appropriate Cramer decision tree to select with regards to the most adequate, safe human exposure level.