A three-layered backpropagation neural network was developed to differentiate malignant from benign brain tumors in a group of patients with astrocytic gliomas. The MRI findings of 43 patients were reviewed before biopsy by three neuroradiologists independently. This provided a database made up of 129 patients' records each of which comprised 13 parameters derived from pre- and post-contrast MR images. The network's generalizing ability was then tested to predict the outcome of biopsy in 36 new cases and its performance compared to that of radiologist using ROC analysis. The output of the network with and without radiologists' impression yielded a better diagnostic performance with relative ROC areas of 0.94 and 0.91, respectively; compared to 0.84 obtained by radiologist. These results demonstrate that the neural network can effectively differentiate malignant from benign brain tumors.