A smart wireless sensor network analytics requirement, beyond routine data collection, aggregation and analysis, in large-scale applications, is the automatic classification of emerging unknown events (classes) from the known classes. In this paper we present a new form of SVM that combines multiclass classification and anomaly detection into a single step to improve performance when data contains vectors from classes not represented in the training set. We demonstrate how the concepts of structural risk minimisation and anomaly detection are combined and analysing the effect of the various training parameters. The evaluations on several benchmark datasets reveal its ability to accurately classify unknown classes and known classes simultaneously.