Small bowel polyps present in images acquired by wireless capsule endoscopy are more difficult to detect using computer-aided diagnostic (CAD) systems. We aimed to identify the optimum morphological characteristics that best describe a polyp and convert them into feature vectors used for automatic detection of polyps present in images acquired by wireless capsule endoscopy (WCE). We prospectively included 54 patients with clinical indications for WCE. Initially, physicians analyzed all images acquired, identifying the frames that contained small bowel polyps. Subsequently, all images were analyzed using an automated computer-aided diagnostic system designed and implemented to convert physical characteristics into vectors of numeric values. The data set was completed with texture and color information, and then analyzed by a feed forward back propagation artificial neural network (ANN) trained to identify the presence of polyps in WCE frames. Overall, the neural network had 93.75% sensitivity, 91.38% specificity, 85.71% positive predictive value (PPV) and 96.36% negative predictive value (NPV). In comparison, physicians' diagnosis indicated 94.79% sensitivity, 93.68% specificity, 89.22% PPV and 97.02% NPV, thus showing that ANN diagnosis was similar to that of human interpretation. Computer-aided diagnostic of small bowel polyps, based on morphological features detection methods, emulation and neural networks classification, seems efficient, fast and reliable for physicians.