Complex target recognition tasks rarely succeed through the application of just one classification scheme. Using the combination/fusion of different classifiers based on Inverse Synthetic Aperture Radar (ISAR) images usually explore complementary information. Thus, the each individual classifier results will be combined in order to improve the global recognition rate. Automatic target recognition systems mostly employ fusion strategies for this aim. The empirical evidence of the effectiveness of this approach makes it of the main current directions in target recognition research. In this paper, the recognition combination will be presented using fuzzy fusion based on three classifiers: K- nearest Neighbors, Support Vector Machines and Multi-Layer Perceptron classifiers. In this purpose, we have used Sugeno and Mamdani models. To improve our approach, we have used an ISAR image database which was reconstructed from an anechoic chamber. All results of all individual and combined classifiers will be presented.