Microarrays are a new technology that allows biologists to better understand the interactions between diverse pathologic state at the gene level. However, the amount of data generated by these tools becomes problematic, even though data are supposed to be automatically analyzed (e.g., for diagnostic purposes). The issue becomes more complex when the expression data involve multiple states. We present a novel approach to the gene selection problem in multi-class gene expression-based cancer classification, which combines support vector machines and genetic algorithms. This new method is able to select small subsets and still improve the classification accuracy.