The computational properties of a wireless sensor network (WSN) have been investigated by implementing a fully distributed pattern recognition algorithm within the network. It is shown that the set up allows a physical object to develop a capability, which to some extent may be considered similar to our sense of touch, with the WSN acting as an artificial nervous system in this regard. The effectiveness of the algorithm is inspected by comparing the outputs from the sensors with the stress patterns generated through a simple finite element model and then stored within the network. It is shown that the test object could successfully differentiate between its internal stress states resulting from the changes to its external loading conditions. Suitability of the algorithm is discussed with respect to the data storage requirement per node of the WSN.