Video-based surveillance systems may benefit from the integration with microphone arrays for the localization of sound events. Applying the sound localization techniques to the surveillance of large areas requires addressing some open issues, such as the non uniform resolution of the microphones-based localization systems. This paper presents a new method for tracking moving sound events based on an Hidden Markov Model (HMM), which exploits a priori information derived from medium and longterm observations of the monitored area. The results obtained with simulated trajectories show that the HMM-based tracker is able to significantly reduce the localization error. Applications can be found in surveillance systems for large areas, such as square, streets, or parking lots, where it is of interest the monitoring of moving vehicles and people.