Conventional methods to test for credit ratings of financial debt issuers based on current means of classification are typically undertaken in the framework of applied statistical methods. In this paper, a newly introduced approach, Support Vector Machines (SVMs), has been applied to test a set of Standard & Poor (S&P)'s issuers' credit rating data. The primary purpose of this credit rating analysis is to measure the credit worthiness of credit securities' issuers and thus provide investors valuable information in making financial decisions. To construct our classification model, the ten key financial variables used by S&P's, and a dummy country variable, are used as the input variables. A conventional full-order neural network based classification model is selected as the benchmark. Our findings indicate the superiority of the SVMs approach over the neural network approach.