A long-standing goal of machine learning is to build a system which can detect a large number of classes with accuracy and efficiency. Some relationships between classes would become a scale-free network in which we can classify the assigned class very fast. Many available methods for multiclass problems have been proposed in the literatures, such as AdaBoost.ECC , AdaBoost.ERP,  and JointBoost . However, many of them are inaccurate or time-consuming on training. In this paper, we propose a new algorithm, called AdaBoost.ERC, which combines the approach of Dietterich and Bakiri  based on error correcting output codes (ECOC) and Shapire’s boosting algorithm  . With advantages of both concepts, our new approach achieves better performance compared to AdaBoost.ECC, AdaBoost.ERP, and JointBoost.