A multisensor feature-based fusion approach to target recognition using a framework of model-theory is proposed. The best discrimination basis algorithm (BDBA) based on the best basis selection technique and the sensory data fusion system (SDFS) based on logical models and theories are applied for feature extraction. The BDBA selects the most discriminant basis. The SDFS first selects features, which are interpretable in terms of symbolic knowledge about the domain, from the most discriminant basis determined for each sensor separately. Then, it fuses these features into one combined feature vector. The SDFS uses formal languages to describe the domain and the sensing process. Models represent sensor data, operations on data, and relations among the data. Theories represent symbolic knowledge about the domain and about the sensors. Fusion is treated as a goal-driven operation of combining languages, models, and theories related to different sensors into one combined language, one combined model of the world and one combined theory. The results of our simulations show that the recognition accuracy of the proposed automatic multisensor feature based recognition system (AMFRS) is better than the recognition accuracy of a system that performs recognition using most discriminant wavelet coefficients (MDWC) as features. The AMFRS utilizes a model-theory framework (SDFS) for feature selection, while MDWC are selected from all the most discriminant bases determined for each sensor using a relative entropy measure.