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Most of the problems for data management in today's wireless sensor networks were already dealt with during the past thirty years of the artificial neural-networks tradition and that kind of algorithms can be easily implemented to wireless sensor network platforms. These problems include the need for simple parallel distributed computation, possibility for distributed storage, fault-tolerance and in some cases the possibility of auto-classification of sensor readings. We will present data acquisition through hierarchical two-level architecture with algorithms which will use wavelets for initial data-processing of the sensory inputs and neural-networks which use unsupervised learning for categorization of the sensory inputs. They are tested on a data obtained from a set of 4 motes, equipped with seven sensors each.