Acoustic　emission　(AE)　of　316L　stainless　steel　with　of　low　Ni　content　shows,　under　tension,　simultaneously　three　avalanche　processes.　One　avalanche　process　relates　to　the　movement　of　dislocations,　the　others　to　martensitic　transformations　and　detwinning/twinning.　Detwinning/twinning　occurs　predominantly　at　the　early　stage　of　the　plastic　deformation　while　martensitic　transformations　only　become　observable　after　large　plastic　deformation.　All　processes　coincide　during　deformation　with　variable　effect　on　AE.　An　excellent　fingerprint　for　the　detection　of　the　coincidence　between　the　several　mechanisms　is　the　observation　of　multivalued　E　~　A2　correlations.　AE　signals　from　moving　dislocations　decay　more　slowly　(~　7×10−3s)　and　show　long　avalanche　durations.　In　contrast,　AE　signals　during　martensitic　transformations　and　detwinning/twinning　decay　rapidly　(−4s)　and　show　short　avalanche　durations.　They　can　be　distinguished　by　different　energy　exponents　of　their　avalanches.　The　energy　distributions　of　the　mechanisms　differ　because　energies　are　defined　as　the　integral　over　the　squared　AE　amplitudes,　where　the　integration　extends　over　the　avalanche　durations.　A　combination　of　statistical　analysis　with　Convolutional　Neural　Network　calculations　provides　an　accurate　and　straightforward　method　for　online,　non-destructive　avalanche　monitoring　of　strain-induced　martensitic　transformations　in　316L　steel　under　high　strain.　©　2021