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High-throughput mass spectrometry technologies, such as surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-ToF-MS), generate large sets of complex data. The high dimensionality of these datasets poses analytical and computational challenges to the task of spectrum classification. In this paper, we describe a fast pattern recognition system for SELDI-ToF mass spectra, which hones in on spectrum subsets with high discriminatory power. The system incorporates a new filter for removal of common characteristics and noise. Our method is demonstrated on a set of 215 SELDI-ToF mass spectra of serum samples from ovarian cancer patients. We show that our system can extract the discriminatory subsets, and that the use of the new filter improves classification accuracy and computational speed.