Growing environmental concerns have motivated businesses to carefully assess the environmental impact of their products and services at all stages of a life-cycle. Reverse logistics plays an important role in achieving "green supply chains" by providing customers with the opportunity to return the warranted and/or defective products to the manufacturer. An efficient reverse logistics structure may lead to a significant return on investment as well as a significantly increased competitiveness in the market. In order to ensure efficiency, many organizations outsource their reverse logistics activities by engaging third-party logistics providers that implement reverse logistics programs designed to gain value from returned products. The selection of third-party providers is a crucial step in initializing reverse logistics related practices. This study aims to efficiently assist the decision makers in determining the "most appropriate" third-party reverse logistics provider using a two-phase model based on artificial neural networks and fuzzy logic in a holistic manner. A numerical example is also included in the study to demonstrate the steps of the proposed model. (c) 2007 Elsevier Ltd. All rights reserved.