Background: Strong health systems are fundamental if countries are to improve health outcomes and accelerate the attainment of the Sustainable Development Goal (SDGs) number 3 ‘Ensure healthy lives and promote well-being for all at all ages.’ Despite the strong consensus on need to strengthen health systems, many health systems lack the capacity to measure or understand their own weakness and constraints which effectively leaves policy makers without ideas of what they should actually strengthen. Methods: Principal Component Analysis (PCA) was used to factor weights which were used to assess individual contribution of indicators to the health system performance. PCA is a type of a multivariable linear regression of all indicators in one model. PCA index was classify variables from heighted to the lowest indicator and further used to rank the indicator. Indicators of individual health system building block were weighted independently to measure the amount of contribution to the respective health system building block. The weights were then aggregated to produce individual health system building block indices which were the independent variables in the multivariable linear regression model. Coefficients of the regression was used to assess marginal effects and p-value<0.05 were considered statistics significant result Results: Service delivery (p<0.0001), health financing (p<0.0001), health workforce (p=0.005) and medical supplies and commodities (p<0.0001) had significant effect on service provision. Health governance was not a significant factor influencing service provision. Conclusions: Among the health system building blocks that significantly influenced service provision were service delivery, health workforce, and health financing and medical supplies. This is the first study to the best of the knowledge of the researcher to apply principal component analysis, to analyze health system performance in a devolved system Kakamega. The method provides opportunity for future application in health systems analysis even in absence of comparative data Keywords: Principal Component Analysis, Health Systems DOI : 10.7176/JHMN/59-03