To improve ultrasonographic diagnosis of prostate cancer, the authors evaluated the performance of an optimized backpropagation artificial neural network (ANN) in predicting an outcome (cancer-not cancer) from recorded information on patients admitted for transrectal ultrasonography (TRUS) performed in our Center. A total of 442 cases with complete information were selected for the study. After preselecting 17 variables (age, PSA, previous clinical diagnosis, and 14 ultrasonographic ones) through univariate analysis, a randomly selected subset of data (50%) was used to train ANNs, and the other subset (50%) was used to test the different models. The ANN achieved up to 81.82% of positive predictive value and up to 96.95% of negative predictive value vs. 67.18% and 90.97%, respectively, when compared with those obtained with logistic regression. Results and possible future practical applications are further discussed.