Breast cancer is the most common cancer in women both in the developed and less developed world. It is estimated that worldwide over 508 000 women died in 2011 due to breast cancer (Global Health Estimates, WHO 2013). Although breast cancer is thought to be a disease of the developed world, almost 50% of breast cancer cases and 58% of deaths occur in less developed countries (GLOBOCAN 2008). Breast cancer survival rates vary greatly worldwide, ranging from 80% or over in North America, Sweden and Japan to around 60% in middle-income countries and below 40% in low-income countries (Coleman et al., 2008). The low survival rates in less developed countries can be explained mainly by the lack of early detection programmes, resulting in a high proportion of women presenting with late-stage disease, as well as by the lack of adequate diagnosis and treatment facilities. Kenya, like many other developing countries is faced with double burden of diseases. Communicable diseases have escalated over the years like HIV, TB and Malaria. On the other hand, non-communicable diseases like cancer, hypertension and diabetes among others have increased. All these diseases put a strain on the scarce government resources. One of the challenges being how to equitably finance health care services across population groups. Mammography screening is the only screening method that has proven to be effective. Although there is evidence that organized population-based mammography screening programmes can reduce breast cancer mortality by around 20% in the screened group versus the unscreened group across all age groups, in general there appears to be a narrow balance of benefits compared with harms, particularly in younger and older women. There is uncertainty about the magnitude of the harms – particularly over diagnosis and overtreatment. Mammography screening is very complex and resource intensive and no research of its effectiveness has been conducted in low resource settings. Missing in much of the literature is the end user’s monetary valuation of the health interventions or services. In health, like many other fields, decision makers are often faced with the challenging balance of ensuring equitable access to services especially for vulnerable low income populations while at the same time avoiding setting prices that are too low to sustain programs, leading to over reliance on external funding. Knowledge of individuals’ monetary valuation of the benefits derived from such health interventions would aid in setting optimal pricing levels for services. However, health interventions are not subject to the normal economic market for goods and services, making it difficult to value benefits derived thereof. Among the methodologies used to elicit individual’s monetary valuations of program benefits include willingness to pay (WTP) studies. The theoretical foundations of willingness to pay as a measure of commodity and service value are rooted in consumer demand theory (Bala MV, 1999). Individual WTP values point to consumer choice behavior or preferences with regard to particular goods or services. An aggregation of individual WTP values is expected to generate aggregate consumer demand for the particular good or service. Building on the quality adjusted life year (QALY) measurement, WTP studies in the health sector attempt to elicit a dollar value from people for a good/service that is not subject to the market pricing mechanisms. Preferences are weighted on money, health and time with immediate and higher impact interventions expected to be valued higher than interventions whose outcome is expected at a future date and those deemed to have a lower impact. WTP estimates therefore enable a more comprehensive valuation of benefits than the commonly used quality adjusted life year (QALY). The QALY is a utility-based, cardinal, interpersonally comparable and time-dependent measure of effectiveness based on preferences over health and time (Klose, 2002). The WTP methodology has been used widely in environmental assessments and marketing to determine individuals’ monetary valuation of services and; suitable pricing levels for commodities (Ortuzar et al., 2000; Israel & Arik, 2004; Breidert et al., 2006; Gillespie & Bennet, 2011) . The methodology is gaining acceptance and use in determining the economic value of health interventions and commodities (Olsen and Cam, 1998; Bala, Josephine, & Lisa, 1999; Raab et al., 2002; Winfrey, 2002; Cookson, 2003; King et al., 2005; Yeung et al., 2005; Hideo et al., 2006; Trapero-Bertan et al., 2013; Chakraborty, 2013). The objective of this study is to assess the willingness to pay for mammogram in government health facilities in Kenya. This will be a cross-sectional descriptive study which will employ contingency valuation. The sample size will be 384 women attending health facilities in Kiambu County in Kenya.