A major limitation in identifying peptides from complex mixtures by shotgun proteomics is the ability of search programs to accurately assign peptide sequences using mass spectrometric fragmentation spectra (MS/MS). Manual analysis is used to assess borderline identifications; however, it is error-prone and time consuming, and criteria for acceptance or rejection are not well defined. Here we report a Manual Analysis Emulator (MAE) program which evaluates results from search programs by implementing two commonly used criteria: (1) consistency of fragment ion intensities with predicted gas phase chemistry, and (2) whether a high proportion of the ion intensity (PIC = Proportion of Ion Current) in the MS/MS can be derived from the peptide sequence. To evaluate chemical plausibility, MAE utilizes similarity (Sim) scoring against theoretical spectra simulated by MassAnalyzer software (Zhang, Z., Anal. Chem. 2004, 76:1002-8) using known gas phase chemical mechanisms. The results show that Sim scores provide significantly greater discrimination between correct and incorrect search results than achieved by Sequest XCorr scoring or Mascot Mowse scoring, allowing reliable automated validation of borderline cases. To evaluate PIC, MAE simplifies the DTA text files summarizing the MS/MS spectra and applies heuristic rules to classify the fragment ions. MAE output also provides data mining functions, which are illustrated by using PIC to identify spectral chimeras, where two or more peptide ions were sequenced together, as well as cases where fragmentation chemistry is not well predicted.