A memristor-based neuromorphic radionuclide identification system is proposed and tested for robustness. The computational task consists of classifying an incoming radionuclide signal from a dictionary of well-known radionuclides. Nuclide identification accuracy was determined by performing a defect-oriented testing of the system. Defect analysis and modelling focused on static faults, where the memristor resistivity was stuck at extreme values.