In this study, a multidimensional fractionation approach was combined with MS/MS to increase the capability of characterizing complex protein profiles of mammalian neuronal cells. Proteins extracted from primary cultures of cortical neurons were digested with trypsin followed by fractionation using strong cation exchange chromatography. Each of these fractions was analyzed by microcapillary reversed-phase LC-MS/MS. The analysis of the MS/MS data resulted in the identification of over 15,000 unique peptides from which 3,590 unique proteins were identified based on protein-specific peptide tags that are unique to a single protein in the searched database. In addition, 952 protein clusters were identified using cluster analysis of the proteins identified by the peptides not unique to a single protein. This identification revealed that a minimum of 4,542 proteins could be identified from this experiment, representing approximately 16% of all known mouse proteins. An evaluation of the number of false-positive identifications was undertaken by searching the entire MS/MS dataset against a database containing the sequences of over 12,000 proteins from archaea. This analysis allowed a systematic determination of the level of confidence in the identification of peptides as a function of SEQUEST cross correlation (Xcorr) and delta correlation (DeltaCn) scores. Correlation charts were also constructed to show the number of unique peptides identified for proteins from specific classes. The results show that low-abundance proteins involved in signal transduction and transcription are generally identified by fewer peptides than high-abundance proteins that play a role in maintaining mammalian cellular structure and motility. The results presented here provide the broadest proteome coverage for a mammalian cell to date and show that MS-based proteomics has the potential to provide high coverage of the proteins expressed within a cell.