The National Cancer Institute currently tests approximately 400 compounds per week against a panel of human tumour cell lines in order to identify potential anti-cancer drugs. We describe several approaches, based on these in vitro data, to the problem of identifying the primary biochemical mechanism of action of a compound. Using linear and non-parametric discriminant procedures and cross-validation, we find that accurate identification of the mechanism of action is achieved for approximately 90 per cent of a diverse collection of 141 known compounds, representing six different mechanistic categories. We demonstrate that two-dimensional graphical displays of the compounds in terms of the initial three principal components (of the original data) result in suggestive visual clustering according to mechanism of action. Finally, we compare the classification accuracy of the statistical discrimination procedures with the accuracy obtained from a neural network approach and, for our example, we find that the results obtained from the various approaches are similar.