Post-Traumatic Stress Disorder (PTSD) and mild Traumatic Brain Injury (mTBI) affect soldiers returning from recent conflicts at an elevated rate. Our study focuses on the use of magnetic resonance spectroscopy (MRS) measurements to distinguish subjects having mTBI, PTSD, or both, with the goal of identifying biomarkers for of these specific disorders from the MRS data. MRS provides a non-invasive in vivo technique for measuring the concentration of metabolites in the brain, thus serving as a “virtual biopsy” that can be used to monitor a range of neurological diseases. The traditional method for analyzing MRS data assumes that the signal arises from a known set of metabolites and finds the best fit to a collection of pre-defined basis functions representing this set. Our novel approach makes no assumptions about the underlying metabolite population, and instead extracts a rich set of wavelet-based features from the entire MRS signal. Capturing the structure of all significant peaks in the signal allows for the discovery of previously unknown signatures related to disease state. We applied this approach to MRS data from 100 participants across five categories: civilian control subjects, military control subjects, military with PTSD, military with mTBI, and military with both PTSD and mTBI. After signal processing to remove artifacts, features were extracted from each signal using a wavelet decomposition approach, and MRS features from subjects with PTSD, mTBI, or both, were compared to both military and civilian control subjects. Our analysis identified significant changes in many different regions of the MR spectrum, including regions corresponding to glutamate, glutamine, GABA, Creatine, and Lactate. Classifiers based on these features exhibit correct classification rates of 80% or better in cross-validation, demonstrating the value of MRS as a non-invasive means of measuring biochemical signatures associated with PTSD and mTBI in military service men and women.