Abstract Objective. The subthalamic nucleus (STN) is the most selected target for the placement of the Deep Brain Stimulation (DBS) electrode to treat Parkinson’s disease. Its identification is a delicate and challenging task which is based on the interpretation of the STN functional activity acquired through microelectrode recordings (MERs). Aim of this work is to explore the potentiality of a set of 25 features to build a classification model for the discrimination of MER signals belonging to the STN. Approach. We explored the use of different sets of spike-dependent and spike-independent features in combination with an ensemble trees classification algorithm on a dataset composed of 13 patients receiving bilateral DBS. We compared results from six subsets of features and two dataset conditions (with and without standardization) using performance metrics on a leave-one-patient-out validation schema. Main results. We obtained statistically better results (i.e. higher accuracy p-value = 0.003) on the RAW dataset than on the standardized one, where the selection of seven features using a minimum redundancy maximum relevance algorithm provided a mean accuracy of 94.1%, comparable with the use of the full set of features. In the same conditions, the spike-dependent features provided the lowest accuracy (86.8%), while a power density-based index was shown to be a good indicator of STN activity (92.3%). Significance. Results suggest that a small and simple set of features can be used for an efficient classification of MERs to implement an intraoperative support for clinical decision during DBS surgery.