The continuous emerging of peer-to-peer (P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2P applications monitoring, in particular, P2P tra?c classifi cation, is becoming increasingly important. In this paper, we propose a novel approach for accurate P2P tra?c classifi cation at a fi ne-grained level. Our approach relies only on counting some special fl ows that are appearing frequently and steadily in the tra?c generated by specifi c P2P applications. In contrast to existing methods, the main contribution of our approach can be summarized as the following two aspects. Firstly, it can achieve a high classifi cation accuracy by exploiting only several generic properties of fl ows rather than complicated features and sophisticated techniques. Secondly, it can work well even if the classifi cation target is running with other high bandwidth-consuming applications, outperforming most existing host-based approaches, which are incapable of dealing with this situation. We evaluated the performance of our approach on a real-world trace. Experimental results show that P2P applications can be classifi ed with a true positive rate higher than 97.22%and a false positive rate lower than 2.78%.