Stereotactic neurosurgery for Parkinson's disease (PD) is one of the most used treatments for relief symptoms of this degenerative disorder. Current methods include ablation and deep brain stimulation (DBS) that can be applied to the various nuclei in the basal ganglia (BG), for instance to the Subthalamic nucleus (STN) or the Ventral medial nucleus (Vim). Identification of thus regions must be rigorous and within a minimum position error. Usually, skilled specialist identifies the brain area by comparing and listening to the rhythm created by the temporal and spatial aggregation of action potentials presented in microelectrode recordings (MER). We present a novel system for automatic identification of the various nuclei in the BG which addresses the limitations of the subjectivity and the non-stationary nature of MER signals. This system incorporates the time-frequency analysis using the Hilbert-Huang Transform (HHT), which is a recent tool for processing nonlinear and non-stationary data, with a dynamic classifier based on Hidden Markov Models (HMM). Classification accuracy in two different databases is compared to validate the performance of the proposed method. Results show that system can recognize selected nuclei with a mean accuracy of 90%.