Body Area Sensor Networks (BASN) are an emerging technology enabling the design of natural Human Computer Interfaces (HCI) in the context of Ambient Intelligence. This class of interactive applications poses new challenges on sensor network design that are hard to be faced using traditional solutions optimized for environmental monitoringlike applications. In this paper we present a novel solution for wireless and wearable posture recognition based on a custom-designed wireless body area sensor network, called WiMoCA. Nodes of the network, mounted on different parts of the human body, exploit tri-axial accelerometers to detect body postures. Afterwards we discuss results of interactive performance and power consumption optimizations required to match application constraints.