Previous work in data fusion has seen the development of a range of architectures for multi-sensor data fusion systems, from fully centralised through distributed to fully decentraiised [1, 2]. This paper presents some further experimental results obtained from an implementation of a multi-target tracking system built around a fully decentralised Kalman filter (DKF) [3, 4]. Here we concentrate on the problem of sensor management, and consider how the individual sensors in a decentraiised sensing network can use the information in the global picture to make decisions about which targets to observe. Explicit use is made of the information available locally to a sensor to control its pointing and target detection. The sensor modality (e.g. range only, bearing only, etc.) strongly affects the way in which the sensor is managed. The tracking system integrates an essentially range-only sensor with a bearing-only sensor . The sensors run asynchronously from each other, and also exhibit asynchronous first detection. These effects are studied in the context of known target motion, as is the temporary removal of one of the sensors from the system. Of particular importance is the effect limited communications bandwidth has on the timeliness of the information exchange. Possible applications of the work are discussed, and suggestions are made for further research. This paper is organised as follows. First we review some background material, including the decentralised data fusion test bed used for the experiments reported here. Then we address the sensor management problem, and describe the experiments we have performed; these focus on assessing the impact on the performance of the data fusion system as a whole of employing sensor management. Finally, we draw some conclusions, and indicate some possible future work.