Proteomic analysis using mass spectrometry data is a powerful tool for biomarker discovery. However, proteomic data suffers from two crucial problems i/ are inherently very noisy and ii/ the number of features that finally characterize each spectrum is usually very large. In the present study, a well-established framework of data preprocessing steps was developed to deal with the problem of noise, incorporating smoothing, normalization, peak detection, and peak alignment algorithms. In addition, to alleviate the problem of feature dimensionality, a novel iterative peak selection method was developed for choosing peaks (features) from the preprocessed spectra, based on sequential global thresholding followed by particle swarm optimization. These features were fed into a probabilistic neural network algorithm, in order to discriminate healthy from prostate cancer cases and, thus, to determine, through the algorithm's optimal design, biomarkers related to prostate cancer.