Abstract: Reducing the computational complexity is indispensable for the pattern identification in according to the integrative spectrum data. A PCA-based integrative spectrum identification method is therefore proposed. It regards an N-point spectrum as a point in the N-dimension space and forms a data matrix by use of the known spectrum as samples. Then, after PCA, the number of directions of principal components satisfying the threshold values of information remaining to reduce the dimensions of high dimensional data. And the centers of projective points of various spectrum in low dimensional space are computed to obtain the corresponding data templates. In applications, the identification results are classified according to the criterion which implies the shortest distance. The results of numerical simulation and voice recognition reveal that the method proposed has stable performance with high accuracy of identification and may take effect in its applications.