Anomaly detection has become more and more difficult for telecommunication network due to the various trends of networking technologies and the growing number of unauthorized activities in the performance data. This paper builds up a performance management system based on the one-class-support vector machine (OCSVM) and K-means clustering algorithm, which achieves not only the automatic detection of network anomalies but also the clustering of the anomalies with different levels. The OCSVM detects the anomalies by solving an optimal problem to separate the nominal data from the anomalies; these detected anomalies are then classified into minor, medium and severe levels using K-means clustering. The real telecommunication performance data are employed in this paper for the investigation, and the numerical results demonstrate the promising performance of this system.