We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.