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Hepatotoxicity is a major cause of pharmaceutical drug attrition and is also a concern within other chemical industries. In silico approaches to the prediction of hepatotoxicity are an important tool in the early identification of adverse effects in the liver associated with exposure to a chemical. Here, we describe work in progress to develop an expert system approach to the prediction of hepatotoxicity, focussing particularly on the identification of structural alerts associated with its occurrence. The development of 74 such structural alerts based on public-domain literature and proprietary data sets is described. Evaluation results indicate that, whilst these structural alerts are effective in identifying the hepatotoxicity of many chemicals, further research is needed to develop additional structural alerts to account for the hepatotoxicity of a number of chemicals which is not currently predicted. Preliminary results also suggest that the specificity of the structural alerts may be improved by the combined use of applicability domains based on physicochemical properties such as log P and molecular weight. In the longer term, the performance of predictive models is likely to benefit from the further integration of diverse data and prediction model types.