The　small　leak　in　the　propulsion　system　pipeline　of　the　sounding　rocket　is　prone　to　occur　in　the　connections　because　of　the　screw　thread　loosening.　Due　to　economic　and　technical　bottleneck,　the　traditional　soap　bubble　method　is　widely　used　in　practice　to　evaluate　whether　existing　a　leak　or　not　by　visually　observing　the　bubble＇s　size　and　numbers.　Thus　doing　so　will　result　in　the　low　inspection　efficiency　and　high　cost.　Using　acoustic　emission　(AE)　techniques,　this　paper　presents　an　experimental　study　on　small　leak　detection　on　the　screw　thread　connection　in　the　propulsion　system　pipeline　of　sounding　rocket.　The　time　and　frequency　characteristics　of　the　corresponding　small　leak　AE　signals　are　investigated.　After　characteristic　indices　extraction　and　selection,　the　multi-class　support　vector　machines　(MCSVM)-based　leak　rates　recognition　algorithm　in　One-vs-All　(OVA)　is　proposed.　It　has　been　validated　that,　for　the　propulsion　system　pipeline　of　the　sounding　rocket,　the　dominant　characteristic　frequency　band　of　the　small　leak　AE　signals　induced　by　screw　thread　loosening　concentrates　on　35-45　kHz.　The　proposed　optimal　OVA　SVM　models　can　achieve　good　classification　accuracy　of　＞98%　by　using　the　characteristic　index　set　Envelope　area,　standard　deviation　(STD),　root-mean-square　(RMS),　Energy,　Average　frequency　and　Gaussian　Radial　Basis　Function　(RBF)　kernel　function.　The　drastic　drops　in　the　false　alarm　attribute　to　use　the　combination　of　time-　and　frequency-domain　characteristic　indices.　Especially,　once　adding　the　＇Envelope　area＇　into　the　characteristic　index　set,　the　classification　accuracies　of　the　OVA　SVM　models　are　further　improved　significantly　regardless　of　the　effect　of　kernel　functions.　©　2013　IEEE.