Categorical regression is a mathematical tool that can be adapted to estimate potential health risk from chemical exposures. By regressing ordered categories of toxic severity or pathological staging on exposure dose, this method can estimate the likelihood of observing any of the categories of severity at any dose level. Depending on the nature of the available data, these estimates can take the form of incidence rates for any of the categories in an exposed population or the probability of a new study conducted at a specified dose level being classified as one of the categories. Categorical regression is illustrated using toxicity data on aldicarb. For aldicarb, the data fall into three different groups: human clinical studies, dietary exposures in experimental animals, and accidental human exposure by contaminated crops. The U.S. EPA has assessed this literature and developed a reference dose (RfD) of 0.001 mg/kg-day. The results of applying categorical regression to data from human clinical studies suggests a maximum likelihood risk estimate of adverse effects of 0.008% at a 10-fold higher dose than the RfD when blood cholinesterase inhibition is not considered as an adverse effect. When blood cholinesterase inhibition of 20% or more is considered as an adverse effect, a maximum likelihood risk estimate of adverse effects is 0.1% at a dose 10-fold higher than the RfD.