This paper reports on the development of support vector machines for polarimetric radar target classification. Automatic classification of radar signatures of man-made objects has been shown to be improved noticeably by optimum selection of transmitting and receiving states of polarization. However, the optimum polarization states may be target-dependent. Thus the need for an optimum classifier to correctly label radar signatures associated with different targets when sensed at differing pairs of transmitting and receiving polarization states. To generate radar signatures of targets at various transmit-receive pairs of polarization angles from vertical and horizontal states, the technique of polarization synthesis was applied. Then statistical attributes from each radar signature were used for its representation. Finally, support vector machines, using a number of kernels such as linear functions, polynomials, Gaussian radial basis functions, exponential basis functions, linear splines, and basis spline functions, were developed and used on real fully polarimetric radar data. The results indicate that a small subset of polarization angles are sufficient for generating signatures needed for training a classifier for optimal separation of polarimetric-diverse signatures. Moreover, the classification performance in terms of receiver operating characteristic curves shows that Gaussian kernels outperform other kernels that we have used.