In our previous works, a recognition system named Re- sifCar was designed specifically for on-line handwritten character recognition. This system is based on an explicit modeling by hierarchical fuzzy rules. Thus, it is understand- able an optimizable after the learning stage. We present in this article a new classifier that is an extension of Resif- Car. Indeed it tries to combine ResifCar’s advantages with a generic aspect to handle different recognition problems. This new hybrid system combines two complementary lev- els. The first one uses a robust modeling by an intrinsic fuzzy clustering of each class and determines their confus- ing areas. The second level, based on fuzzy decision trees, operates a progressive discrimination inside these areas. Both levels are formalized by fuzzy inference systems or- ganized hierarchically and fused for final decision. Experi- ments were conducted on the one hand on classical bench- marks and on the other hand on on-line handwritten digits and lower-case letters. For all of these cases, the classifier achieves good recognition rates without final optimization.