¹⁶O/¹⁸O labeling is one differential proteomics technology among many that promise diagnostic and prognostic biomarkers of disease. While the incorporation of ¹⁸O in the C- terminal carboxyl during endoproteinase digestion in the presence of H₂¹⁸O makes the process of labeling facile, the ease and effectiveness of label incorporation have in some regards been outweighed by the difficulties in interpreting the resulting spectra. Complex isotope patterns result from the composition of unlabeled (¹⁸O₀), singly labeled (¹⁸O₁) and doubly labeled species (¹⁸O₂) as well as contributions from the naturally occurring isotopes (e.g., ¹³C, ¹⁵N). Moreover, because labeling is enzymatic, the number of ¹⁸O atoms incorporated can vary from peptide to peptide. Finally, it is difficult to distinguish highly up-regulated from highly down-regulated or C-terminal peptides. We have developed an algorithm entitled Regression Analysis Applied to Mass Spectrometry (RAAMS) that automatically, rapidly and confidently interprets spectra of ¹⁸O labeled peptides without requiring chemical composition information derived from product-ion spectra. The algorithm is able to measure the effective ¹⁸O incorporation rate due to variable enzyme substrate specificity of the pseudo-substrate during the isotope exchange reaction, and corrects for the ¹⁸O₀ abundance that remains in the labeled sample when using a two step digestion/labeling procedure. We have also incorporated a method for distinguishing pure ¹⁸O₀ from pure ¹⁸O₂ peptides utilizing impure H₂¹⁸O. The algorithm operates on centroided peak lists and is therefore very fast: nine chromatograms containing, on average, 6,761 isotopic clusters were interpreted in 45 sec each. RAAMS is fast enough (average 38 msec/spectrum) to allow the possibility of performing information- dependent MS/MS on a chromatographic time scale on species exceeding predetermined ratio thresholds. We describe in detail the operation of the algorithm and demonstrate its use on datasets with known and unknown ratios.