The main objective of this paper is to introduce a hybrid model of Self Organizing Maps (SOM) and C4.5, to reduce the costs while maintaining an acceptable diagnostic performance. In this hybrid model, SOM is used first to form clusters and then C4.5 trees specific to each cluster is constructed. The proposed hybrid model is tested on multiclass Thyroid Data and compared to standalone C4.5 tree. Costs were reduced by 22%-27% and performance results vary between 88% and 97% in terms of accuracy and 90%-97% in terms of sensitivity. Cost and performance differences between the hybrid model and standalone C4.5 found to be statistically significant according to Wilcoxon signed-rank test.