A new method for phylogenetic classification of bacterial strains using matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) is proposed. This method was developed using a bioinformatics-based approach to the rapid identification of bacteria as previously proposed by Demirev and co-workers, which uses ribosomal proteins composed of approximately 50 subunit proteins as biomarkers. Although the amino acid sequences of ribosomal proteins are highly conserved, slight sequence variations can occur at the strain level. Since ribosomal subunit proteins are a complex of housekeeping proteins that have different phylogenetic evolution rates, sequence variation detected as mass differences by MALDI-MS may be useful for the phylogenetic classification of bacteria at strain level. In our proposed method, the first step is the selection of reliable biomarkers through characterization of the expressed ribosomal subunit proteins of a reference strain (usually a genome-sequenced strain) by MALDI-MS. The observed masses in the MALDI mass spectra of cell lysates of sample strains are then compared with the biomarker masses of the reference strain. The biomarkers for each sample strain were designated as present or absent at the reference masses, indicated by 1 or 0, respectively, which were summarized in a table. This table is processed by cluster analysis, generating a phylogenetic tree. In this study, the success of this approach was confirmed by classification of Pseudomonas putida strains because its classification is much more complicated than that of other bacterial strains. Forty-three reliable biomarkers were selected from ribosomal sub-unit proteins of a genome-sequenced strain, P. putida KT2440. The numbers and kinds of biomarkers observed for 16 strains of P. putida, including different biovars, were markedly different, reflecting the variety of the strains. The classification results by the proposed method were highly comparable to those based on the DNA gyrase subunit B gene (gyrB) sequence analysis, suggesting our proposed method would be a useful high-throughput method for phylogenetic classification of newly isolated bacteria.