Advent of immunotherapies has revolutionized cancer treatment. Recent success with immunotherapy is predominantly due to checkpoint inhibitors that block inhibitory signals and enable T cell activation that target cancer cells. Other strategies including adoptive cell transfer and cancer vaccines are being investigated in parallel to increase available arsenal for immune therapy. Cancer genome sequencing has revealed thousands of somatic mutations across various cancers. It is known that some proteins encoded by mutated genes are processed and presented on the cell surface. These MHC presented mutant peptides serve as neo-antigens that are recognized by T cells. Identification of such neo-antigens can strengthen cancer immunotherapy efforts and provide a set of antigens that can be targeted. Currently, various prediction programs are employed to predict potential neo-antigens based on cancer genome sequencing data. However, this approach can result in significant number of false positives and false negatives. Recent studies have demonstrated mass spectrometry based unbiased approaches to identify MHC bound peptides. We have combined whole-exome sequencing analysis with MHC peptidome mass spectrometry to identify potential neo-antigens from melanoma cell lines. By analyzing our MHC peptidome dataset and publicly available datasets, we have identified sequence features and other rules that potentially determine MHC presentation of peptides. These observations can prove useful for developing better experimental strategies and prediction tools to identify potential cancer neo-antigens. Reliable identification of cancer neo-antigens can accelerate development of novel therapeutic approaches that can exploit host immune system to treat cancers.