Characterizing the immune response towards a pathogen is of high interest for vaccine development and diagnosis. However, the characterization of disease-related antigen-antibody interactions is of enormous complexity. Here, we present a method comprising binding studies of serum antibody pools to synthetic random peptide libraries, and data analysis of the resulting binding patterns. The analysis can be applied to classify and predict different groups of individuals and to detect the peptides which best discriminate the investigated groups. As an example, the analysis of antibody repertoire binding patterns of different mice strains and of mice infected with helminth parasites is shown. Due to the design of the library and the sophisticated analysis, the method is able to classify and predict the different mice strains and the infection with very high accuracy and with a very small number of peptides, illustrating the potential of random library screenings in determining molecular markers for diagnosis.