New methodologies for surveillance and identification of Mycobacterium tuberculosis are required to stem the spread of disease worldwide. In addition, the ability to discriminate mycobacteria at the strain level may be important to contact or source case investigations. To this end, we are developing MALDI-TOF MS methods for the identification of M. tuberculosis in culture. In this report, we describe the application of MALDI-TOF MS, as well as statistical analysis including linear discriminant and random forest analysis, to 16 medically relevant strains from four species of mycobacteria, M. tuberculosis, M. avium, M. intracellulare, and M. kansasii. Although species discrimination can be accomplished on the basis of unique m/z values observed in the MS fingerprint spectrum, discrimination at the strain level is predicted on the relative abundance of shared m/z values among strains within a species. For the 16 mycobacterial strains investigated in the present study, it is possible to unambiguously identify strains within a species on the basis of MALDI-TOF MS data. The error rate for classification of individual strains using linear discriminant analysis was 0.053 using 37 m/z variables, whereas the error rate for classification of individual strains using random forest analysis was 0.023 using only 18 m/z variables. In addition, using random forest analysis of MALDI-TOF MS data, it was possible to correctly classify bacterial strains as either M. tuberculosis or non-tuberculous with 100% accuracy.