A novel method of forming a training set to yield acceptable classification results is addressed in this paper. Since the development of several sophisticated satellites, remotely sensed data classification has become important in environmental study. The two major classification approaches are the neural approach and the conventional statistical one. The prior and crucial process carrying out any classification technique is the selection of the training samples forming the learning based. To be successful, the learning base must be representative enough of the studied region. However, the more the land cover resolution of the satellites increases, the more difficult it is to cope with these conditions. In this study, an incremental learning base forming process is presented. The method is based on the `small and growing' concept. From a small data base constituted carefully and manually by the selection of a few small areas for each class, spectral and contextual criteria are defined. Furthermore, the number of detected classes is validated in order to take into account all the important categories to be classified. Finally, the criteria are associated with the initial base and the initial classification to incorporate new patterns in the learning data base. The proposed method is flexible enough to form a good learning base, and proves to be successful in any complex image. Moreover, addition only is required to form the learning base, and not, like in other similar incremental methods, revised learning set through merger, deletion or addition.