Small and Micro Enterprises play a vital role in job creation and make significant contributions to economic growth in developed and developing economies alike. However, one on the main challenges of small and micro enterprise has been access to finances and capital. This study therefore sought to evaluate the factors influencing Small and Micro Enterprises credit worthiness by commercial Banks in Nakuru Town, Kenya. The specific objectives looked at: firm’s ownership, firm’s financial performance, credit information sharing and central bank regulations to determine how they affect credit worthiness of Small and Micro Enterprises from the perspective of commercial banks. The limitation of the study was that not all respondent respondents responded to the questionnaires positively with fear that information provided will expose them, besides the bankers are very busy with a lot of restricted information sharing. The study was guided by modern portfolio theory, theory of credit scoring and competitive pricing of default risk and pecking order theory. The study was conducted among commercial banks in Nakuru Town using the survey research design. The target population was 68 bank staff involved in Small and Micro Enterprise lending who comprised of 34 credit managers and 34 Small and Micro Enterprise loans/relations officers; a census design was used to select all the members of the target population for study. The study used questionnaire as a collection tool of primary data. To ensure the validity of instruments, they were subjected to a pilot test in selected Bank Branches in Naivasha Town. The pilot questionnaires were then analyzed using the Cronbach reliability coefficient to determine the extent of reliability. Data collected was coded and analyzed with the aid of computer programs. Quantitative data was analyzed using descriptive statistics which include frequencies and percentages. Chi square (x2 ) analysis was also done to determine how individual factors affect creditworthiness; influence of the independent variables on the dependent variable was then computedusing the multiple regression analysis.