BACKGROUND:  While current best-practices in PRO development include evaluation of the relative ease of translation for global trial use prior to instrument finalization, methodologies for this translatability assessment (TA) vary greatly.  In the proposed approach, representative languages (RLs) are selected to assess the translation difficulty of PRO concepts without the time and cost of evaluating multiple languages with shared characteristics. METHODS: In the genealogical approach employed by linguists, languages sharing a common ancestor that become separated by geographical or socio-political boundaries will evolve in distinct ways, resulting in sets of languages (families) with common linguistic features (e.g.  word order, phrasal structure, morphology, lexical items, etc.).  Because of this relative similarity within language groups,  efficiency can be gained by assessing translatability with sets of appropriately-selected representative languages, which can in turn predict translation problems likely to affect others in their linguistic families.  As such, use of appropriate criteria for the selection the RLs is of key importance. RESULTS: Selection of RLs should be based both on linguistic properties and other features salient to outcomes research.   A family or group of languages may also be defined by shared characteristics that are not purely linguistic in nature.  Features such as geographic and cultural (religious/dietary/social) aspects, number and distribution of speakers worldwide, and criteria related to health care utilization or study implementation should be considered in the definition of language families/groups and in the selection of RLs.  CONCLUSIONS:  Despite differences that undeniably exist between individual languages, limited information can be gained by the repetitive assessment of prospective translation difficulty within groups of languages having similar characteristics.  Instead, the use of a representative language to assess translation difficulty for a related group of languages provides greater resource efficiency and more effective application of TA in providing important feedback prior to finalization of newly developed measures.