In　this　paper,　we　propose　the　joint　use　of　discrete　wavelet　transform　(DWT)-based　feature　extraction　and　probabilistic　neural　network　(PNN)　classifier　to　classify　tissues　using　gene　expression　data.　In　the　feature　extraction　module,　gene　expression　data　are　firstly　transformed　into　time-scale　domain　by　DWT　and　then　the　reconstructed　signals　by　using　wavelet　transform　are　reduced　to　a　lower　dimensional　feature　space.　In　the　module　of　tissue　classification,　the　outputs　of　the　extractor　are　fed　into　it　PNN　classifier,　and　the　class　labels　are　given　finally.　Some　test　and　comparison　experiments　have　been　made　to　evaluate　the　performance　of　the　proposed　classification　scheme,　using　the　features　extracted　with　as　well　as　without　wavelet　transform　processing　procedure.　Correct　rates　of　92%　and　98.7%　in　tumour　vs.　normal　classification　have　been　obtained　using　the　proposed　scheme　on　two　well-known　data　sets:　a　colon　cancer　data　set　and　1　human　lung　carcinomas　data　set.　(c)　2005　Elsevier　B.V.　All　rights　reserved.