For many neurological disorders, including traumatic brain injury (TBI), neuroimaging information plays a crucial role determining diagnosis and prognosis. TBI is a heterogeneous disorder that can result in lasting physical, emotional and cognitive impairments. Magnetic Resonance Imaging (MRI) is a non-invasive technique that uses radio waves to reveal fine details of brain anatomy and pathology. Although MRIs are interpreted by radiologists, advances are being made in the use of deep learning for MRI interpretation. This work evaluates a deep learning model based on a residual learning convolutional neural network that predicts TBI severity from MR images. The model achieved a high sensitivity and specificity on the test sample of subjects with varying levels of TBI severity. Six outcome measures were available on TBI subjects at 6 and 12 months. Group comparisons of outcomes between subjects correctly classified by the model with subjects misclassified suggested that the neural network may be able to identify latent predictive information from the MR images not incorporated in the ground truth labels. The residual learning model shows promise in the classification of MR images from subjects with TBI.