Antigenic peptides (termed T cell epitopes) are assembled with major histocompatibility complex (MHC) molecules and presented on the surface of antigen-presenting cells (APCs) for T cell recognition. T cells engage these peptide-MHCs using T cell receptors (TCRs). Because T cell epitopes determine the specificity of a T cell immune response, their prediction and identification are important steps in developing peptide-based vaccines and immunotherapies. In recent years, a number of computational methods have been developed to predict T cell epitopes by evaluating peptide-MHC binding; however, the success of these methods has been limited for MHC class II (MHCII) due to the structural complexity of MHCII antigen presentation. Moreover, while peptide-MHC binding is a prerequisite for a T cell epitope, it alone is not sufficient. Therefore, T cell epitope identification requires further functional verification of the MHC-binding peptide using professional APCs, which are difficult to isolate, expand, and maintain. To address these issues, we have developed a facile, accurate, and high-throughput method for T cell epitope mapping by screening antigen-derived peptide libraries in complex with MHC protein displayed on yeast cell surface. Here, we use hemagglutinin and influenza A virus X31/A/Aichi/68 as examples to describe the key steps in identification of CD4+ T cell epitopes from a single antigenic protein and the entire genome of a pathogen, respectively. Methods for single-chain peptide MHC vector design, yeast surface display, peptide library generation in Escherichia coli, and functional screening in Saccharomyces cerevisiae are discussed.