Rapid advances in mass spectrometry have positioned it as a prime tool for diagnosis and biomarker discovery. Distilling a handful of accurate disease markers from the thousands of mass-to-charge ratios that comprise a mass spectrum raises non-trivial data analytic challenges. Thus, mass-spectral analysis is turning more and more to data mining, a technology that lies at the crossroads of artificial intelligence and statistical data analysis. This review describes recent attempts at applying data mining techniques to extract diagnostic biomarkers for cancer from SELDI-TOF and MALDI-TOF mass spectra.