This work presents a method for creating a mass spectral library containing tandem spectra of identifiable peptide ions in the tryptic digestion of a single protein. Human serum albumin (HSA 1 ) was selected for this purpose owing to its ubiquity, high level of characterization and availability of digest data. The underlying experimental data consisted of ϳ3000 one-dimensional LC-ESI-MS/MS runs with ion-trap fragmentation. In order to generate a wide range of peptides, studies covered a broad set of instrument and digestion conditions using multiple sources of HSA and trypsin. Computer methods were developed to enable the reliable identification and reference spectrum extraction of all peptide ions identifiable by current sequence search methods. This process made use of both MS2 (tandem) spectra and MS1 (electrospray) data. Identified spectra were generated for 2918 different peptide ions, using a variety of manually-validated filters to ensure spectrum quality and identification reliability. Shotgun proteomics is a widely used and evolving method for determining the protein composition of a biological mixture (1-3). It most often involves the digestion of denatured proteins by trypsin, followed by the identification of product peptides and the use of this information to infer protein identities and possibly targeted post-translational modifications (PTMs)1 . However, because digestion is a highly complex chemical process, a large proportion of identifiable products are not specifically targeted for analysis and therefore invisible to the analysis. These include unexpected and unwanted peptides that interfere with the analysis. Others may contain modifications of biological origin, which, unless specifically targeted, can be lost among the forest of artifacts (4 -6). This paper describes methods for building a tandem mass spectral library capable of characterizing all identifiable peptides in a tryptic digest of a selected protein. Spectral libraries are known to provide an effective way of reusing this information to quickly, reliably, and sensitively determine peptide identities (7-11). These identifications can serve several purposes, including 1) ensuring that all previously identified peptides are identified regardless of search engine settings, 2) tagging artifact peptides that might otherwise lead to false positive identifications, 3) ensuring the identification of known and identifiable biological post-translational modifications without explicitly looking for them, and 4) providing a list of artifact peptides for assessing the quality of the sample preparation process.HSA, human serum albumin, was selected as the target protein for library development partly because of its ubiquity, making up Ͼ50% of the total protein in blood (12-13) and therefore found in many biological samples, and partly because of the considerable background information available