Biochemical failure, defined here as a rise in the serum prostate specific antigen (PSA) concentration to >0.3 ng/mL or the initiation of adjuvant therapy, is thought to be an adverse prognostic factor for men who undergo radical prostatectomy (RP) as definitive treatment for clinically localized cancer of the prostate (CAP). We have developed an artificial neural network (ANN) to predict biochemical failure that may benefit clinicians and patients choosing among the definitive treatment options for CAP. Clinical and pathologic data from 196 patients who had undergone RP at one institution between 1988 and 1999 were utilized. Twenty-one records were deleted because of missing outcome, Gleason sum, PSA, or clinical stage data. The variables from the 175 remaining records were analyzed for input variable selection using principal component analysis, decision tree analysis, and stepped logistic regression. The selected variables were age, PSA, primary and secondary Gleason grade, and Gleason sum. The records were randomized and split into three bootstrap training and validation sets of 140 records (80%) and 35 records (20%), respectively. Forty-four percent of the patients suffered biochemical failure. The average duration of follow up was 2.5 years (range 0-11.5 years). Forty-two percent of the patients had pathologic evidence of non-organ-confined disease. The average area under the receiver operator characteristic (ROC) curve for the validation sets was 0.75 +/- 0.07. The ANN with the highest area under the ROC curve (0.80) was used for prediction and had a sensitivity of 0.74, a specificity of 0.78, a positive predictive value of 0.71, and a negative predictive value of 0.81. These results suggest that ANN models can predict PSA failure using readily available preoperative variables. Such predictive models may offer assistance to patients and physicians deciding on definitive therapy for CaP.