Memristor arrays are promising structures for energy-efficient neuromorphic computing systems. However, due to their nondeterministic fabrication process, manufacturing defects can degrade computation accuracy. In this paper, 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. Results show that the system has a higher tolerance to static defects where the resistance is jammed at the maximum extreme (effective open circuit) than in the minimum extreme (effective short circuit). It is shown that the system maintains close to its full performance when up to 15% random open defects are present in the array. The outcomes of this work are relevant to implementing state-of-the-art memristive devices into similar neuromorphic computing systems.