Improvements in ion trap instrumentation have made n-dimensional mass spectrometry more practical. The overall goal of the study was to describe a model for making use of MS(2) and MS(3) information in mass spectrometry experiments. We present a statistical model for adjusting peptide identification probabilities based on the combined information obtained by coupling peptide assignments of consecutive MS(2) and MS(3) spectra. Using two data sets, a mixture of known proteins and a complex phosphopeptide-enriched sample, we demonstrate an increase in discriminating power of the adjusted probabilities compared with models using MS(2) or MS(3) data only. This work also addresses the overall value of generating MS(3) data as compared with an MS(2)-only approach with a focus on the analysis of phosphopeptide data.