Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The sensitivities to liver and kidney toxicity were 67 and 41%, respectively, whereas the corresponding specificities were 77 and 100%. In some cases, it was not possible to make predictions because of interference by drug-related metabolite signals (18%), an inconsistent histopathological or urinary response (11%), genuine class overlap (8%), or lack of similarity to any other treatment (2%). This study constitutes the largest validation to date of the metabonomic approach to preclinical toxicology assessment, confirming that the methodology offers practical utility for rapid in vivo drug toxicity screening.