Selecting　a　subset　of　marker　genes　from　thousands　of　genes　is　an　important　topic　in　microarray　experiments　for　diseases　classification　and　prediction.　In　this　paper,　we　proposed　a　novel　hybrid　approach　that　combines　gene　ranking,　heuristic　clustering　analysis　and　wrapper　method　to　select　marker　genes　for　tumor　classification.　In　our　method,　we　firstly　employed　gene　filtering　to　select　the　informative　genes;　secondly,　we　extracted　a　set　of　prototype　genes　as　the　representative　of　the　informative　genes　by　heuristic　K-means　clustering;　finally,　employed　SVM-RFE　to　find　marker　genes　from　the　representative　genes　based　on　recursive　feature　elimination.　The　performance　of　our　method　was　evaluated　by　AML/ALL　microarray　dataset.　The　experimental　results　revealed　that　our　method　could　find　very　small　subset　of　marker　genes　with　minimum　redundancy　but　got　better　classification　accuracy.　©　2007　IEEE.