Isolating the variable or set of variables responsible for an out-of-control signal is a challenging task in multivariate statistical process control. Several fault isolation approaches have been proposed. However, all assumed a multivariate normal distribution on the process data, an assumption that limits their applicability in many situations. In the present study we propose a nonparametric fault isolation approach based on a hybrid novelty score (HNS). A simulation study was conducted to examine the performance of our proposed HNS-based fault isolation approach, and its results were compared with both parametric and nonparametric T2 decomposition approaches. The performance of our approach was superior in the simulation to either parametric or nonparametric T2 decompositions. This was especially true in nonnormal situations.