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Demand-side Management (DSM) systems have became common in both industrial and homely applications. Basically, these systems help the customers to use electricity more efficiency. Commercial DSM systems are based on the knowledge of instantaneous load power request and, using a priority table, they make their choice. These approaches embed low-level intelligence, hence they can guarantee only coarse results. In this paper an ANN-based residential load classification component to use in the DSM system is described. Aim of the DSM is to prevent cut-off from happening and to schedule loads in a prioritized mode. By means of an associative memory, each socket tap is capable of identify the connected load from a table of "known devices". The eventual misclassification that may arise during the guessing phase is specifically handled by a new training phase. The time the system spends responding to the wrong classification and reacting to it is generally shorter than the time required by the provider's meter to detect the exceeding of the power limit.