Credit ratings serve as indicators for various stakeholders including investors and creditors to assess the risk associated with specific firms or bonds issued by those firms. Sophisticated rating evaluations constitute one of the critical factors that can influence not only individual investment risk but also the financial market as a whole. For this reason, diverse studies on corporate credit rating evaluation have been conducted to date, and recently, research leveraging the superior predictive capabilities of artificial intelligence techniques—particularly artificial neural networks—known for their ability to better capture the characteristics of complex financial data, has been actively pursued. However, artificial neural network techniques have been criticized for several limitations: they require large volumes of training data to estimate input data distributions; they face difficulties in generalization due to the overfitting problem; the initialization procedures needed to avoid local minima depend on experience; and fundamentally, as "black box models," they make it difficult to interpret the model, including the importance of individual variables. In particular, for multi-class classification problems such as corporate bond rating evaluation, data for each rating class can be sparse, making it potentially impossible to build models that require large volumes of training data like artificial neural networks. As a solution to these issues, this study applies the recently spotlighted multi-class support vector machine (SVM) to bond rating evaluation. SVMs have the advantage of being based on clear theoretical foundations, making result interpretation straightforward; they achieve performance levels comparable to artificial neural networks in practical applications; and they can rapidly perform classification learning with only a small amount of training data. Moreover, while existing learning algorithms implement the empirical risk minimization principle, SVMs are based on the structural risk minimization principle, which allows them to largely avoid the overfitting problem. To verify these possibilities, this study applied multi-class SVM to the case of Korean corporate bond rating evaluation. To test its superiority over other comparative models, its performance was compared with artificial neural networks and multiple discriminant analysis. The analysis results confirmed that multi-class SVM demonstrated statistically significantly superior performance differences compared to the other comparison methods.