Expulsion　identification　is　of　significance　for　welding　quality　assessment　and　control　in　resistance　spot　welding.　In　order　to　improve　the　identification　accuracy,　a　novel　wavelet　decomposition　and　Back　Propagation　(BP)　neural　networks　with　the　peak-to-peak　amplitude　and　the　kurtosis　index　were　proposed　to　identify　the　expulsion　from　electrode　force　sensing　signals.　The　rapid　step　impulse　and　resultant　damping　vibration　of　electrode　force　was　determined　as　a　robust　indication　of　expulsion,　and　this　feature　was　extracted　from　the　electrode　force　waveform　by　seven-layer　wavelet　decomposition　with　Daubechies5　wavelets.　Then,　the　energy　distribution　proportion　of　the　decomposed　detail　signals　were　calculated,　and　the　highest-energy　one　was　selected　as　the　target　signal.　Two　statistical　indexes　were　introduced　in　this　paper　to　measure　the　target　signal　in　overall　situation　and　volatility.　The　bigger　the　peak-to-peak　amplitude　is,　the　more　violent　the　fluctuation　is.　Moreover,　the　higher　the　kurtosis　index　is,　the　stronger　the　impact　is,　and　the　lower　the　dispersion　degree　of　the　data　is.　Experimental　analysis　showed　that　neither　the　peak-to-peak　amplitude　nor　the　kurtosis　index　could　accurately　judge　the　expulsion　defect　individually,　because　of　the　early　signal　fluctuation,　likely　affected　by　the　work-piece　clamping,　work-piece　clearance,　or　the　oxide　film　thickness.　Therefore,　the　BP　neural　networks　were　introduced　to　identify　the　expulsion　defects,　which　is　a　mature　and　stable　non-linear　pattern　recognition　method.　Testing　experiments　presented　good　results　with　the　trained　networks　and　improved　the　evaluable　accuracy　effectively　in　the　quality　assessment　of　the　resistance　spot　welding.