Multivariate Time Series (MTS) data obtained from large scale systems carry resourceful information about the internal system status. Multivariate Time Series Clustering is one of the exploratory methods that can enable one to discover the different types of behavior that is manifested in different working periods of a system. This knowledge can then be used for tasks such as anomaly detection or system maintenance. In this study, we make use of the statistical method, Variable Order Markov Models (VOMMs) to model each individual MTS and employ a new metric to calculate the distances between those VOMMs. The pairwise distances are then used to accomplish the MTS Clustering task. Two other MTS Clustering methods are presented and the superiority of the proposed method is confirmed with the experiments on two data sets from Cyber-Physical Systems. The computational complexity of the presented methods is also discussed.