In practical imaging condition, the silhouette of a 3D moving target is changing, therefore its recognizability is variable. In this paper several definitions are given, such as dynamic feature space of patterns and dynamic recognizability of patterns. Necessity of multi-scale feature models of 3D target and rationality of using the general constraint for recognizing target image sequence are discussed. Based on these discussions, a multi-scale intelligent recursive recognizer (MUSIRR) is proposed for recognizing 3D moving targets, in which BP neural network and RBF neural network are the basic cells. During training,regular moment invariants of the multi-scale binary characteristic views of the target model are used as the pattern feature-vector. During recognition, the algorithm sufficiently uses reasonable restrictions of imaging process and target poses which are not changed acutely to achieve a good recognition ratio. Compared with the algorithms based on single-scale characteristic view models in references, the training of the MUSIRR algorithm is easy and needs less samples composed of the target characteristic view models. The algorithm can not only treat single frame images but also treat image sequence more effectively. The rationality and validity of the approach are proved by the results of massive simulation experiments onseveral kinds of aircrafts.