Tandem mass spectrometry (MS/MS) is one of the leading proteomics technologies, applicable to a wide range of experiments involving composition analysis of protein mixtures. Currently only ∼10-20% of MS/MS spectral data lead to the successful peptide identifications, and the rate of false positives remains to be high. We propose Probability Profile Method (PPM) as a new route for the development of MS/MS data analysis algorithms. The principal idea can be described as a probabilistic "labeling" of the individual peaks, or as a detailed analysis of the spectra leading to peak separation into specific categories (b-ion, y-ion, double charged b-ion, etc). PPM "assignments", conducted on large and diverse data sets (∼60,000 spectra), indicate that a large majority of MS/MS peaks can be identified with a surprising level of confidence, providing the foundation for a range of novel algorithmic approaches: spectra can be edited by selecting desirable peak categories: overall characteristics of MS/MS spectra, such as parent ion charge or total number of the present ions, can be rapidly estimated with a high precision; labeled peaks of the same category (e.g. b-ions) can be efficiently connected into de novo tag peptides.