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In this paper, we analyze the potential of combining wireless sensor networks with artificial neural networks (ANNs) to build a "smart forest-fire early detection sensory system" (SFFEDSS). We outline our new SFFEDS system in which temperature, light and smoke data from low-cost sensor nodes spread out on the forest bed is aggregated into information. This information is spatially and temporally labeled into knowledge which will be encoded as input to ANN models that convert it into intelligence. At the top tier of our system, the trained neural models make intelligent decisions and report fire in its early stages based on gathered field knowledge. In our experimentation, we extended the sensing capability of the MicaZ sensor motes by attaching external smoke detectors of our own design. The results are very promising as the SFFEDSS unit is able to not only detect fire but also accurately report the direction of fire progress which is deduced from the wind direction.