Due to the asymmetry of information between borrowers that are small- or medium-sized enterprises (SMEs) and lenders (banks), many banks are considering this sector as a risky sector. It is crucial for banks to be able to distinguish healthy from risky companies in order to reduce their nonperforming assets in the SME sector. If they can do this, lending and financing to SMEs through banks will be easier with lower collateral requirements and lower interest rates. In this paper, we provide a scheme originally developed by Yoshino and Taghizadeh-Hesary (2014) for assigning credit ratings to SMEs by employing two statistical analysis techniques - principal component analysis and cluster analysis - applying 11 financial ratios of 1,363 SMEs in Asia. If used by the financial institutions, this comprehensive and efficient method could enable banks and other lending agencies around the world, and especially in Asia, to group SME customers based on financial health, adjust interest rates on loans, and set lending ceilings for each group.