Distributed agile software development is the most popular and trending software development life cycle model in IT industry. Distributed agile software development (DASD) integrates the principles of agile software development (ASD) and distributed software development (DSD). Although many benefits are achieved through the adoption of DASD in software development, numerous risk factors emerge due to contrary properties of ASD and DSD. However, all these risk factors are not equally important. This calls for a need to prioritize these risks based on their severity. This paper proposes a novel risk prioritization technique based on artificial neural network to prioritize the risks for Distributed Agile software development. The proposed work has been validated on the dataset of 110 risks. The reported results are then compared with the existing risk assessment techniques.