The　aim　of　our　study　was　to　recognize　results　of　surface　electromyography　(sEMG)　recorded　under　conditions　of　a　maximum　voluntary　contraction　(MVD)　and　fatigue　states　using　wavelet　packet　transform　and　energy　analysis.　The　sEMG　signals　were　recorded　in　10　young　men　from　the　right　upper　limb　with　a　handgrip.　sEMG　signals　were　decomposed　by　wavelet　packet　transform,　and　the　corresponding　energies　of　certain　frequencies　were　normalized　as　feature　vectors.　A　back-propagation　neural　network,　a　support　vector　machine　(SVM),　and　a　genetic　algorithm-based　SVM　(GA-SVM)　worked　as　classifiers　to　distinguish　muscle　states.　The　results　showed　that　muscle　fatigue　and　MVC　could　be　identified　by　level-4　wavelet　packet　transform　and　GA-SVM　more　accurately　than　when　using　other　approaches.　The　classification　correct　rate　reached　97.3%　with　seven　fold　cross-validation.　The　proposed　method　can　be　used　to　adequately　reflect　the　muscle　activity.