A new method using oil atomic spectrometric analysis technology to monitor the mechanical wear state was proposed. Multi-dimensional time series model of oil atomic spectrometric data of running-in period was treated as the standard model. Residues remained after new data were processed by the standard model. The residues variance matrix was selected as the features of the corresponding wear state. Then, high dimensional feature vectors were reduced through the principal component analysis and the first three principal components were extracted to represent the wear state. Euclidean distance was computed for feature vectors to classify the testing samples. Thus, the mechanical wear state was identified correctly. The wear state of a specified track vehicle engine was effectively identified, which verified the validity of the proposed method. Experimental results showed that introducing the multi-dimensional time series model to oil spectrometric analysis can fuse the spectrum data and improve the accuracy of monitoring mechanical wear state.