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Inspired by perception in biological systems, distribution of a massive amount of simple sensing devices is gaining more support in detection applications. A focus on fusion of sensor signals instead of strong analysis algorithms, and a scheme to distribute sensors, results in new issues. Especially in wearable computing, where sensor data continuously changes, and clothing provides an ideal supporting structure for simple sensors, this approach may prove to be favourable. Experiments with a body-distributed sensor system investigate the influence of two factors that affect classification of what has been sensed: an increase in sensors enhances recognition, while adding new classes or contexts depreciates the results. Finally, a wearable computing related scenario is discussed that exploits the presence of many sensors.