In order to successfully implement an asset management program, an accurate and reliable deterioration model for assets should be available. Deterioration models are considered as the basis for predicting and prioritizing future maintenance, rehabilitation, or replacement activities of assets. Sewer agencies are seeking different methods to prioritize inspection of sewer pipes in presence of financial constraints and deteriorating pipelines. This paper presents the development of a defect based deterioration model using Bayesian belief network (BBN) in sewer pipelines to be used in inspection prioritization. Different types of defects found in an existing sewage network were collected from closed circuit television (CCTV) inspection reports and used in creating the model to determine the likelihood of a sewage pipeline to be in a certain condition state. The BBN is used to generate dependency between different defects and their effect on the overall condition of the pipe. Monte-Carlo simulation (MCS) was introduced to eliminate the uncertainties that could arise in the model due to independent events that would be propagated through the BBN to assess the final dependent posterior probabilities. BBN is considered as an efficient tool because it deals with inherent uncertainties and handles complex interdependencies using conditional probabilities. The developed model could be used as a decision support tool by which decision makers could plan inspection of deteriorated sections.