Automatic pulmonary nodule detection in computed tomography (CT) images has been a challenging problem in computer aided diagnosis (CAD). Most recent recognition methods based on support vector machines (SVMs) have shown difficulty in achieving balanced sensitivity and accuracy. To improve overall performance of SVM based pulmonary nodule detection, a mixed kernel SVM method is proposed for recognizing pulmonary nodules in CT images by combining both Gaussian and polynomial kernel functions. The proposed mixed kernel SVM, together with a grid search for parameters optimization, can be tuned to seek a balance between sensitivity and accuracy so as to meet the CADs need, and eventually to improve learning and generalization ability of the SVM at the same time. In our experiments, thirteen features were extracted from the candidate regions of interest (ROIs) preprocessed from a set of real CT samples, and the mixed kernel SVM was trained to recognize the nodules in the ROIs. The results show that the proposed method takes into account both the sensitivity and accuracy compared to single kernel SVMs. The sensitivity and accuracy of the proposed method achieve 92.59% and 92% respectively.