This research presents methods for detecting and isolating faults in multiple Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) configurations. Traditionally, in the inertial technology, the task Fault Detection and Isolation (FDI) is realized by the parity space method. However, this approach performs poorly with low-cost MEMS-IMUs, although, it provides satisfactory results when applied to tactical or navigation grade IMUs. In this article, we propose a more complex approach to detect outliers that takes into account the shape and size of multivariate data. The proposed method is based on Mahalanobis distances. Such approach has already been successfully applied in other fields of applied multivariate statistics, however, it has never been tested with inertial sensors. As Mahalanobis distances (as well as the parity space method) is very sensitive to the presence of the same outliers this method aims to detect, we propose using its robust version. The performances of the proposed algorithm are evaluated using dynamical experiments with several MEMS-IMUs and a reference signal provided by a tactical-grade IMU run in parallel. The conducted experiment shows that, for example, the percentage of false alarms is approximately ten times lower when using a method based on Mahalanobis distances as compared to that based on the parity space approach.