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Target classification capability using unattended ground sensors (UGS) has been a fatal factor for its operational performance. The sensor observations are significantly affected by external conditions. When the context is time-varying the usage of the same classifier may not be a good way to perform target classification. Dynamic Data Driven (DDD) method is proposed for dynamically extract and use the knowledge of context as feedback in order to adaptively choose the appropriate classifiers and thereby enhance the target classification performance. A context evolution model is constructed as Deterministic Finite State Automata (DFSA) and, for every context state in this DFSA, an event classifier is trained to classify the targets.