Bacterial typing is of great importance in clinical diagnosis, environmental monitoring, food safety analysis, and biological research. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is now widely used to analyze bacterial samples. Identification of bacteria at the species level can be realized by matching the mass spectra of samples against a library of mass spectra of known bacteria. Nevertheless, in order to reasonably type bacteria, identification accuracy should be further improved. Herein, we propose a new framework to the identification and assessment for MALDI-MS based bacterial analysis. Our approach combines new measures for spectra similarity and a novel bootstrapping assessment. We tested our approach on a general data set containing the mass spectra of 1741 strains of bacteria and another challenging data set containing 250 strains, including 40 strains in the Bacillus cereus group that were previously claimed to be impossible to resolve by MALDI-MS. With the bootstrapping assessment, we achieved much more reliable predictions at both the genus and species level, and enabled to resolve the Bacillus cereus group. To the best of the authors’ knowledge, our method is the first to provide a statistical assessment to MALDI-MS based bacterial typing that could lead to more reliable bacterial typing.