Artificial neural networks (ANNs) are techniques of nonlinear data modeling that have been studied in a wide variety of medical applications. An ANN was developed to assist in the diagnosis of acute rejection in liver transplant recipients. We investigated the diagnostic accuracy of this ANN on a new data set of patients from the same hospital. In addition, we compared the diagnostic accuracy of the ANN with that of the individual input variables (alanine aminotransferase [ALT] and bilirubin levels and day posttransplantation). Clinical and biochemical data were collected retrospectively for 124 consecutive liver transplantations (117 patients) over the first 3 months after transplantation. Diagnostic accuracy was calculated using receiver operating characteristic (ROC) curve analysis. The ANN differentiated rejection from rejection-free episodes in the new data set over the first 3 months posttransplantation with an area under the ROC curve of 0.902 and sensitivity and specificity of 80.0% and 90.1% at the optimum decision threshold, respectively. The ANN was significantly more specific than ALT or bilirubin level or day posttransplantation at their corresponding optimum decision thresholds (P <.0001). Peak ANN output occurred 1 day earlier than peak values for either ALT or bilirubin (P <.005). The diagnostic accuracy of the ANN was greater than that of any of the individual variables that had been used as inputs. It would be a useful adjunct to conventional liver function tests for monitoring liver transplant recipients in the early postoperative period.