The ability of physicians to effectively treat and cure cancer is directly dependent on their ability to detect cancers at their early stages. The early detection of cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins has been reported as a promising method to achieve this goal. In this paper, we analyzed the ovarian cancer and prostate cancer data sets using support vector machine (SVM) to detect cancer at the early stages based on serum proteomic pattern. The results showed that SVM, in general, performed well on these two data sets, as measured by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Linear kernel worked the best on ovarian cancer data with a sensitivity of 0.99 and an accuracy of 0.97, while polynomial kernel worked the best on prostate cancer data with a sensitivity of 0.79 and an accuracy of 0.82. When redial kernel was applied to either of the two data sets, all the samples were predicted as cancer samples, with a sensitivity of 1 and a specificity of 0. Furthermore, feature selection did not improve SVM performance.