This paper presents the modelling possibilities of probabilistic neural networks to a complex real-world problem, i.e. credit rating modelling. First, current approaches in credit rating modelling are introduced. Then, probabilistic neural networks are designed to classify US companies and municipalities into rating classes. The input variables are extracted from financial statements and statistical reports in line with previous studies. These variables represent the inputs of probabilistic neural networks, while the rating classes from Standard & Poor's and Moody's rating agencies stand for the outputs. Classification accuracies, misclassification costs, and the contributions of input variables are studied for probabilistic neural networks compared to other neural networks models. The results show that the rating classes assigned to bond issuers can be classified accurately with probabilistic neural networks using a limited subset of input variables.
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