NMR spectra of urine from rats treated with a range of liver, kidney and testicular toxins at various doses were measured and classified using neural network methods. Toxin-induced changes in the levels of 18 low molecular weight endogenous urinary metabolites were assessed using a simple semi-quantitative scoring system. These scores were used as input to an artificial neural network, the use of which has been explored as a means of predicting the class of toxin. With this limited data set, based only the level of the maximal changes of these 18 metabolites, the network was able to predict the class and hence target organ of the toxins. Renal cortical toxicity was well predicted as was liver toxicity. The few examples of renal medullary toxins in the data set resulted in relatively poor training of the network although correct classification was still possible.