Pipelines are considered safest mode of transport because of their limited number of facilities. It is therefore very important to monitor and optimize their operation and reduce their facilities to acceptable limits. Hence, it is an immediate requirement to assess and predict condition of existing oil and gas pipelines and to prioritize the planning of their inspection on a timely basis. Therefore, this study presents the development of models based on specific factors, that can predict the condition of onshore oil and gas pipelines. The model was developed using BPN (Back Propagation Network) techniques based on historical inspection data collected from the oil and gas fields. The model is expected to help pipeline operators to assess the condition of existing oil and gas pipelines and hence prioritize their inspection and rehabilitation operations.