AbstractThis paper proposes employment of artificial neural network techniques to develop in-network “intelligent computation” and “adaptation” capability for wireless sensor networks to improve their functionality, utility and survival aspects. The goal is to introduce computational intelligence capability for the wireless sensor networks to become adaptive to changes within a variety of operational contexts and to exhibit intelligent behaviour. The characteristics of wireless sensor networks bring many challenges, such as the ultra large number of sensor nodes, dense deployment, changing topology structure, and the most importantly, the limited resources including power, computation, storage, and communication capability. All these require the applications and protocols running on wireless sensor network to be not only energy-efficient, scalable and robust, but also “adapt” to changing environment or context, and application scope and focus among others, and demonstrate intelligent behaviour. Feasibility of the proposed approach is demonstrated through a simulation-based case study which entailed a clustering of Iris data using Kohonen's self-organizing map neural network which was embedded across a wireless sensor network.