Artificial neural networks are computer-based statistical models that can be used to imitate biologic neural processes. They have been applied to a variety of problems in medicine, including diagnosis and outcomes predictions. In the area of urologic oncology, these neural networks have been used to assist in the diagnosis of prostate cancer, predicting response to therapy and recurrence in prostate cancer, predicting the presence of renal cell carcinoma in cystic renal lesions, and predicting the presence of occult metastatic disease in nonseminomatous testicular germ cell tumors. This article reviews the basic concepts of artificial neural networks and summarizes their application in urologic oncology. Neural network technology remains in its infancy in urologic oncology, and many more retrospective and prospective studies are needed to determine its clinical utility.