Clinical diagnosis and treatment decisions for a subset of primary human brain tumors, gliomas, are based almost exclusively on tissue histology. Approaches for glioma diagnosis can be highly subjective due to the heterogeneity and infiltrative nature of these tumors and depend on the skill of the neuropathologist. There is therefore a critical need to develop more precise, non-subjective, and systematic methods to classify human gliomas. To this end, mass spectrometric analysis has been applied to these tumors to determine glioma-specific protein patterns. Protein profiles have been obtained from human gliomas of various grades through direct analysis of tissue samples using matrix-assisted laser desorption ionization mass spectrometry (MS). Statistical algorithms applied to the MS profiles from tissue sections identified protein patterns that correlated with tumor histology and patient survival. Using a data set of 108 glioma patients, two patient populations, a short-term and a long-term survival group, were identified based on the tissue protein profiles. In addition, a subset of 57 patients diagnosed with high-grade, grade IV, malignant gliomas were analyzed and a novel classification scheme that segregated short-term and long-term survival patients based on the proteomic profiles was developed. The protein patterns described served as an independent indicator of patient survival. These results show that this new molecular approach to monitoring gliomas can provide clinically relevant information on tumor malignancy and is suitable for high-throughput clinical screening.