ANN based predictive models have been found to be useful for predicting outcome of different diseases.We evaluated if ANN can predict clinical outcome in patients with LGB based on information typically available during triage. Methods: Data on all patients admitted for LGB during 1998 were prospectively collected. A multi-layered perceptron neural network was constructed using clinical input variables which were available to the clinician during initial evaluation. The ANN was trained by back propagation using data from patients admitted during first half of 1998 (Group I) and then validated using data from patients admitted during the second half of 1998 (Group II). A stepwise logistic regression model was also constructed using available clinical input variables. Moreover, a previously validated scoring system (BLEED score, Crit Care Med 1997) was used to classify patients into high and low risk groups. Rebleeding, death during the same hospital admission and endoscopic, radiological or surgical intervention for control of hemorrhage were the outcome variables used to compare the different predictive models. Results: 141 patients (Mean age 74.9 years, 47 males) were available for analysis. Demographic and clinical features in Group I (n= 75) and Group II (n = 66) were similar. Diverticular bleeding was the predominant cause of bleeding (88, 62%). 7 (4.9%) patients expired, 26 (18.4%) had rebleeding and 21 (14.9%) needed intervention for control of bleeding. Performance of ANN in predicting the outcome variable were significantly better than the BLEED score (McNemar s test, P <0.01) and was comparable to that of the logistic regression based model (Table). Area under the receiver operating curve for ANN were 0.83, 0.87 and 0.91 respectively for rebleeding, intervention and death. Conclusion: ANN can predict outcome of patients with LGB reasonably well and may be used for risk stratification from information available at the time of initial evaluation.