The medical informatics for clustering Sasang types with collected clinical data is important for the personalized medicine, but it has not been thoroughly studied yet. The purpose of this study was to examine the usefulness of neural network data mining algorithm for traditional Korean medicine. We used Kohonen neural network, the Self-Organizing Map (SOM), for the analysis of biomedical information following data pre-processing and calculated the validity index as percentage correctly predicted and type-specific sensitivity. We can extract 12 data fields from 30 after data pre-processing with correlation analysis and latent functional relationship analysis. The profile of Myers-Briggs Type Inidcator and Bio-Impedance Analysis data which are clustered with SOM was similar to that of original measurements. The percentage correctly predicted was 56%, and sensitivity for So-Yang, Tae-Eum and So-Eum type were 56%, 48%, and 61%, respectively. This study showed that the neural network algorithm for clustering Sasang types based on clinical data is useful for the sasang type diagnostic test itself. We discussed the importance of data pre-processing and clustering algorithm for the validity of medical devices in traditional Korean medicine.