Abstract In recent years percutaneous treatments for cancer have won momentum in the medical field. With it new needle insertion robots appeared to overcome the difficulties associated with needle insertion into soft tissue. At first, the main focus was to achieve high needle placement accuracy, however, the focus nowadays has shifted toward needle steering and patient specific needle tissue interaction. In this paper we present a classification method to detect the type of tissue being punctured in real time. The purpose of the proposed method is to detect particular events that can be used in a situational awareness agent. First,wewill introduce themethodology to create the statistical models used for classification, next, we prove the feasibility of the proposed classification method with experimental results and show that the proposed method hit a target even when tissue is deformed by analyzing needle insertion force patterns.