The presence of a large reservoir of untreated sexually transmitted diseases (STDs) in developing countries has prompted a number of suggestions for improving case detection, including the use of clinical algorithms and risk assessments to identify women likely to be infected when they present to clinics for other reasons. We used data from a community-based study of STDs to develop and evaluate algorithms for detection of cervical infection with Chlamydia trachomatis or Neisseria gonorrhoeae, and for detection of vaginal infection with Trichomonas vaginalis or bacterial vaginosis. The algorithms were derived using data from 192 randomly selected women, then evaluated on 200 self-selected women. We evaluated the WHO algorithm for vaginal discharge in both groups. The prevalences of cervical and vaginal infection in the randomly selected group were 27% and 50%, respectively, and 23% and 52%, respectively, in the self-selected group. The derived algorithms had high sensitivities in both groups, but poor specificities in the self-selected women, and the positive predictive values were unacceptably low. The WHO algorithms had extremely low sensitivity for detecting either vaginal or cervical infection because relatively few women reported vaginal discharge. Simple algorithms and risk assessments are not valid for case detection in this population.