The paper discusses a novel algorithm for classifying data represented through multivariate time series based on similarity metrics. To improve over the performance of existent classification methods based on single similarity, the method used in this study is based on a combination between the principal component analysis similarity factor and the average-based Euclidian distance within a fuzzy clustering approach. Additionally, an approach is proposed to cope with the changes of these metrics over the time window, improving the similarity analysis between the objects. The method is applied to the Tennessee Eastman process, a well-known benchmark industrial system used to compare various fault detection and diagnosis approaches. The results were compared with standards multivariate techniques showing the efficiency and flexibility of the proposed method in fault detection and classification problems, when considering different types of failures, process variables, and changes in operating conditions.