To the best of our knowledge, NNAlign is the first web-based bioinformatics solution that allows non-expert users to discover short sequence motifs in quantitative peptide data. As shown here, NNAlign easily competes with state-of-the-art methods for identifying peptidebinding motifs of aligned (exemplified by MHC class I) as well as unaligned (exemplified by MHC class II) quantitative peptide sequence data. Further, demonstrating the general utility of NNAlign, we have used it to characterize the cleavage specificities of proteases from high-throughput peptide array data. If a sufficient number of training examples can be generated, including negative instances, we could envision applying the method also on data generated by phage display peptide libraries. Other instances of recognition of short specific peptide motifs occurs frequently in biology where they are involved in molecular interaction, recognition, signaling, internalization, modification etc (e.g. phosphorylation, dephosphorylation, trafficking motifs, SH2 and SH3 domains, glycosylation, lipidation, etc. In contrast to domain recognition, short linear peptide sequences are thought to be particularly difficult to identify due to their unordered structure [40] . NNAlign appears to be ideally suited to identify such short linear peptide targets. Due to its simple interface and robust performance, we believe the method to constitute a significant tool providing the non-bioinformatician end-user with the ability to perform advanced bioinformatics-driven analysis of largescale peptide data sets.