Clinical studies use a wide variety of health status measures to measure health related quality of life, many of which cannot be used in cost-effectiveness analysis using cost per quality adjusted life year (QALY). Mapping is one solution that is gaining popularity as it enables health state utility values to be predicted for use in cost per QALY analysis when no preference-based measure has been included in the study. This paper presents a systematic review of current practice in mapping between non-preference based measures and generic preference-based measures, addressing feasibility and validity, circumstances under which it should be considered and lessons for future mapping studies. This review found 30 studies reporting 119 different models. Performance of the mappings functions in terms of goodness-of-fit and prediction was variable and unable to be generalised across instruments. Where generic measures are not regarded as appropriate for a condition, mapping does not solve this problem. Most testing in the literature occurs at the individual level yet the main purpose of these functions is to predict mean values for subgroups of patients, hence more testing is required.