The growing number of deployed data mining systems leverage the interest in temporal data anomaly detection. From cyber-security or finance to heart-diseases detection, unexpected data often incorporate critical information that must be analysed. Data anomalies have long been studied from an univariate perspective where only one data dimension changes over time. Few works have been dedicated to multivariate anomaly detection. In this work we provide a comprehensive and structured analysis of the main definitions, state-of-art methods and approaches focusing multivariate temporal data anomaly detection. Our research focus on dealing with variable length data series with millions of samples and multiple feature categories, either static or dynamic, real or categorical valued. We describe a case-study in the maritime domain investigating the unusual spatio-temporal behaviour of commercial vessels and experiment over two open datasets and one got from the MARISA H2020 Project1.