Introduction The measurement and analysis of poverty have traditionally relied on reported income or consumption and expenditure as the preferred indicators of poverty and living standards. Income is generally the measure of choice in developed countries while the preferred metric in developing countries is an aggregate of a household's consumption expenditures, Sahn and Stifel (2003). The choice of expenditures over income is influenced by the difficulties involved in the measuring income in the developing countries. Similarly with the expenditure data the limitation is the extensive data collection which is time- consuming and costly as stated by Vyas and Kumaranayake (2006). In this paper, we construct an asset index using Principal Component Analysis (PCA) from asset ownership variables in the Kenya Demographic and Health Survey (2003) and use logistic regression to identify key determinants of poverty in Kenya. The use of demographic and health survey data to the measure of poverty is not unique. Filmer and Pritchett (2001) used Demographi c and Healthy Survey data to show that the relationship between wealth and enrollment in school can be estimated without income or expenditure data, by using household asset variables. PCA provided acceptable and reliable weights for an index of asset to serve as a measure for wealth. In the four countries examined; India, Indonesia, Nepal and Pakistan this