Focusing　on　a　sharp　decline　in　the　performance　of　endpoint　detection　algorithm　in　a　complicated　noise　environment,　a　new　speech　endpoint　detection　method　based　on　BPNN　(back　propagation　neural　network)　and　multiple　features　is　presented.　Firstly,　maximum　of　short-time　autocorrelation　function　and　spectrum　variance　of　speech　signals　are　extracted　respectively.　Secondly,　these　feature　vectors　as　the　input　of　BP　neural　network　are　trained　and　modeled　and　then　the　Genetic　Algorithm　is　used　to　optimize　the　BP　Neural　Network.　Finally,　the　signal＇s　type　is　determined　according　to　the　output　of　Neural　Network.　The　experiments　show　that　the　correct　rate　of　this　proposed　algorithm　is　improved,　because　this　method　has　better　robustness　and　adaptability　than　algorithm　based　on　maximum　of　short-time　autocorrelation　function　or　spectrum　variance.