Even though most of the methods described above are standard methods for data-driven pattern recognition, the development of a prediction method for any given biological problem is far from straightforward, and the non-expert user will rarely be able to develop their own state-of-the-art prediction methods. We have recently described a neural network-based data driven method, NN-align, which has been specifically designed to automatically capture motifs hidden in unaligned peptide data [20] . NN-align is implemented as a conventional feed-forward neural network and consists of a two-step procedure that simultaneously identifies the optimal peptide-binding core, and the optimal configuration of the network weights (i.e. the motif). This method is therefore inherently designed to deal with unaligned peptide data, and it identifies a core of consecutive amino acids within the peptide sequences that constitute an informative motif. Note that the method does not allow for gaps in the alignment. Although NNalign was originally developed with the unaligned nature of peptide-MHC class II interaction in mind -and independent validations have shown that NN-align indeed performs significantly better than any previously published methods for MHC class II motif recognition [25] -the unique ability of this method to capture subtle linear sequence motifs in quantitative peptide-based data and its adaptability makes it extremely attractive for other applications as well. Here, we have adapted and extended the NNalign method so that it can handle quantitative peptide-based data in general. Making this method generally available for the scientific community, we have embedded it into a public online web-interface that facilitates both handling of input data, optimization of essential training parameters, visual interpretation of the results, and the option of using the resulting method to predict on user-specified proteins/peptides. Through the server the user can easily set up a cross-validation experiment to estimate the predictive performance of the trained method, and automatically reduce redundancy in the data. The logo visualization is also improved with an algorithm that aligns individual neural networks to maximize the information content of the combined alignment. This web-based extension of the NN-align method empowers experimentalists of limited bioinformatics background with the ability to perform advanced bioinformatics-driven analysis of his/ her own sets of large-scale data.