This pilot study investigated the feasibility of translating a quality of life instrument, the Functional Assessment of Cancer Therapy - General version (FACT-G) and the breast cancer version (FACT-B), which consists of the FACT-G plus 10 additional items, into three South African languages (Pedi, Tswana, and Zulu). The international, interdisciplinary research team hypothesized that we could develop reliable and valid translations, and that valuable information could be gleaned from the responses of the three groups of traditional African people, which could inform the Western-trained medical profession. Understanding of cross-cultural views of cancer including its diagnosis and treatment could lead to better communication between the two cultures (Western and Traditional) resulting in increased utilization of Western medical treatment and increased treatment compliance by three of the underserved black populations. A total of 167 respondents completed one of three translated questionnaires, which assessed the patients' quality of life in 5 domains: Physical Well-Being, Social and Family Well-Being, Relationship with Doctor, Emotional Well-Being, and Functional Well-Being, plus for breast cancer patients the additional items on the FACT-B. However, only the items from the FACT-G (the 'core' of the FACT-B) were statistically analyzed for this pilot project. Results showed that it was possible to develop a reliable instrument in the three languages by modifying the standard translation methodology. Translation of physical and functional concepts was most straightforward. Translation of emotional items posed some difficulty. As expected, based upon observations about cultural differences in social values and functioning, the Social/Family Well-Being subscale was problematic. Analysis of this subscale provides information on cultural differences which may be important to physicians desiring to effectively treat this population with sensitivity and dignity. Methodology may be generalizable to other third world patient populations in translation of existing health status questionnaires.