Hair fibers serve as a prevalent and valuable form of biological trace evidence. Taxonomic classification of hair recovered during a criminal investigation provides crucial forensic intelligence and aids in evidence prioritisation. Traditional classification methods rely heavily on the analysis of hair shaft morphology, which requires individual analysis, can have limited taxonomic resolution, and is difficult to scale. Mass spectrometry-based proteomics can provide a biomolecular alternative by use of taxonomic amino acid polymorphisms to resolve the species of origin of trace evidence. In this study a proteomics workflow was developed for the discovery and characterization of taxonomically diagnostic hair peptides. Taxa were selected with a focus on relevance to crime scene investigation. A panel of 226 taxonomically informative peptides were identified and characterized, 209 of which are novel to this study. The biomarker panel includes 59 peptides with apparent single-species resolution, with remaining biomarkers being diagnostic in combination. The study also included 3 positive control keratin sequences where at least 2 are documented to occur in all mammals. The panel of taxa-discriminating markers was subsequently integrated into a forensic workflow, optimized for universal sample preparation and standardised LC/MS/MS data acquisition. The developed workflow was used to analyze single fur-hair shafts (20 mm), from four individuals from each species of interest. The data was further processed using a curated protein database, an efficient and validated bioinformatics workflow. The method was able to classify all hairs analysed with genus or species level resolution without prior source information.