Objective: In order to find effective CT image features of lymph nodes for improving the diagnosis accuracy of metastases and non-metastases tumid lymph nodes in the lung cancer N stage. Method: First, tumid lymph nodes are extracted from chest CT images using interactive segmentation. Second, the multi-resolution histograms of tumid lymph nodes are directly calculated to receive a high-dimensional features sample set with spatial information. Then the classifier for differentiating metastases and non-metastases tumid lymph nodes is constructed with making full use the advantage of SVM. Finally, the performance of classification is evaluated by testing the trained SVM with the test sample set. Result: The test results by 96 cases show that it takes 1.91s for computing 200 dimensional features of 100 lymph nodes, 1.36 s for training and testing the SVM classifier. Receiver Operating Characteristic (ROC) analysis of the classification performance shows that the sensitivity is 76%, specificity is 64%, accuracy is 70%, and the Area Under Curve (AUC) is nearly 0.6525. Conclusion: Image spatial information can effectively express the characteristics of lymph nodes; the classification accuracy of metastases and non-metastases tumid lymph nodes is up to 70% without medical signs. It provides a feasible, simple method for improving the accuracy of the lung cancer N stage.