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This need has driven the development of methods for classification of microorganisms using mass spectrometry, particularly matrix\u2010assisted laser desorption\/ionization mass spectrometry (MALDI\u2010MS), that allows high\u2010throughput analyses with minimum sample preparation. We describe a novel approach to cell typing based on pattern recognition of MALDI mass spectra, which involves charge\u2010state deconvolution in conjunction with a new correlation analysis procedure. The method is applicable to both prokaryotic and eukaryotic cells. Charge\u2010state deconvolution improves the quantitative reproducibility of spectra because multiply charged ions resulting from the same biomarker attaching a different number of protons are recognized and their abundances are combined. This allows a clearer distinction of bacterial strains or of cancerous and normal liver cells. Improved class distinction provided by charge\u2010state deconvolution was demonstrated by cluster spacing on canonical variate score charts and by correlation analyses. Deconvolution may enhance detection of early disease state or therapy progress markers in various tissues analyzed by MALDI\u2010MS. 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