Selecting the appropriate contractor is a significant step to building project success. The correct selection of a competent and suitable contractor may have a positive impact on the outcomes of the works and result in the successful completion of construction projects in time, within budget and with better quality. However, in Ethiopia, the selection of the contractor for a construction project is subjected to uncertainty and influence of criteria other than bid price and thus the results are not completel y consistent. To fill this gap on the problem of contractor selection, this thesis aims to develop a building contractor selection decision making model using an analytical network process. The paper also explains the procedures conducted to formulate a computational model (mathematical model) that can be used to select the most suitable applicant and prioritize competitive contractors for building projects. To achieve these objectives, mixed (qualitative and quantitative) surveys as a research design, purposive sampling as a sampling technique, and both primary and secondary data as a source of data were used. For the primary data, the researcher has used an online questionnaire and interview and for the secondary data document reviews were employed. Among the surveyed, 19 out of 34 (55.88%) respondents (contractors, consultants, clients, and academicians) gave responses. The data gathered was analyzed using Analytical Network Process (ANP). In an Analytical Network Process (ANP) technique, first different selection criteria were identified and grouped into major criteria. The major criteria were weighted and a decision support model was developed and the validity of the developed model was tested in a real project (Case Study). Finally, the results were presented using tables, frequencies, and percentages. It was determined that using the lowest bid value as a sole criterion for selecting the best contractors may not result in an optimum solution. Key words: Analytic network process, analytic hierarchy process, multi-criteria decision- making, contractor selection.