Artificial neural networks and Rough Sets methodology have been utilized to predict human pharmacokinetic elimination half-life data based on animal data training sets. Methylmercury (Hg) pharmacokinetic data was obtained from 37 literature references, which provided data on species, gender, age, weight, route of administration, dose, dose frequency, and elimination half-life based on either whole-body Hg analysis or blood Hg analysis. Data were categorized into various formats for analysis comparisons. Rough Sets methodology was utilized to identify and remove redundant independent variables. Artificial neural networks were used to produce models based on the animal data, which were in turn used to predict and compare to the human elimination half-life values. These neural network predictions were compared to allometric graphical plots of the same data. The best artificial neural network prediction was based on a "thermometer" categorical representation of the data.