The use of a Traffic Matrix(TM) to describe the characteristics of a global network has attracted significant interest in network performance research. Due to the high dimensionality and sparsity of network traffic, Principal Component Analysis(PCA) has been successfully applied to TM analysis. PCA is one of the most common methods used in analysis of high-dimensional objects. This paper shows how to apply PCA to TM analysis and anomaly detection. The experiment results demonstrate that the PCA-based method can detect anomalies for both single and multiple nodes with high accuracy and efficiency. The use of a Traffic Matrix (TM) to describe the characteristics of a global network has already attracted significant interest in network performance research. Due to the high dimensionality and sparsity of network traffic, Principal Component Analysis (PCA) has been successfully applied to TM analysis PCA is one of the most common methods used in analysis of high-dimensional objects. This paper shows how to apply PCA to TM analysis and anomaly detection. The experiment results demonstrate that the PCA-based method can detect anomalies for both single and multiple nodes with high accuracy and efficiency.