The currently established gastric emptying test requires the patient to take a radioactive test meal and to stay under a gamma camera for acquiring abdominal images for 2 hours. It is invasive and expensive. Since the electrogastrogram (EGG) is a cutaneous recording of gastric myoelectrical activity which modulates gastric motor activity, we hypothesized that delayed gastric emptying might be predicted from the EGG using a neural network approach. In this study, simultaneous recordings of the EGG and the emptying rate of the stomach by means of the established method were made in 152 patients with suspected gastric motility disorders. A multilayer feedforward neural network approach for the diagnosis of delayed gastric emptying from the noninvasive EGG was developed. Using 5 spectral parameters of the EGG as inputs, a correct classification of 85% was achieved with an optimized three-layer network. This study indicates that the neural network approach is a potentially useful tool for the noninvasive diagnosis of delayed gastric emptying.