Hazard identification is of vital importance in risk management. It is the first step of undertaking accident likelihood and associated consequence analysis. The traditional hazard identification techniques suffer from being static. New information or evolving conditions cannot be easily incorporated in already identified hazards. To overcome this, the Bayesian network is used to bring dynamics to the hazard identification step. The present work develops a new methodology to map hazard scenarios into the Bayesian network model, which enables real time hazard identification. The model presents a probability ranking for hazards using given input observations. It helps to identify the most credible hazard scenarios for further analysis. Sensitivity analyses are also conducted to investigate the influence of the input parameters on identified hazards. (C) 2016 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.