In this study, we propose an integrated approach that combines Bayesian Networks and Total Cost of Ownership (TCO) to address complexities involved in selecting an international facility for a manufacturing plant. Our goal is to efficiently represent uncertain data and ambiguous information, and to unite them to improve the quality of the decisions. Bayesian Networks provide a framework to elicit information from experts, and provide a structure guide to efficient reasoning, even with incomplete knowledge. Our method is presented in a hierarchical structure so that it can be decomposed into the forms of more manageable units. We consider many tangible and intangible facility location criteria, then these criteria are grouped into few numbers of factors. These factors are then combined to form a cost perspective using the essentials of TCO. (C) 2011 Elsevier Ltd. All rights reserved.