Abstract: Based on the K-L transform and support vector machine, a new recognition algorithm for the 3-D lung nodule was presented to solve the problem that the details of lung nodule were often ignored in the 3-D space when extracting the characteristics of lung nodule in the interested area by the conventional 2-D method. The geometry and intensity features of lung nodule were analyzed and the 3-D features were calculated and extracted to form an original feature space. Then, the K-L transform method was used to exclude the correlativity between features, and the support vector machine (SVM) was introduced to categorize and recognize the potential lung nodules with the receiver operating characteristic (ROC) curve introduced to evaluated the performance of the algorithm proposed. A test involving 36 sets of clinical lung HRCT data was carried out, where the lung nodules were marked with 'golden standard'. The results indicated that the validity of the algorithm or the accuracy of recognition is up to 94.33% and the value of Az (area under the ROC curve) is 0.94.