The　modular　multilevel　converter　with　series　and　parallel　connectivity　was　shown　to　provide　advantages　in　several　industrial　applications.　Its　reliability　largely　depends　on　the　absence　of　failures　in　the　power　semiconductors.　We　propose　and　analyze　a　fault-diagnosis　technique　to　identify　shorted　switches　based　on　features　generated　through　wavelet　transform　of　the　converter　output　and　subsequent　classification　in　support　vector　machines.　The　multi-class　support　vector　machine　is　trained　with　multiple　recordings　of　the　output　of　each　fault　condition　as　well　as　the　converter　under　normal　operation.　Simulation　results　reveal　that　the　proposed　method　has　high　classification　latency　and　high　robustness.　Except　for　the　monitoring　of　the　output,　which　is　required　for　the　converter　control　in　any　case,　this　method　does　not　require　additional　module　sensors.