Abstract.Based on the fact that abnormal states continue prior to the breakage of the fault, an early warning system was developed by monitoring the variables in operation real-time, deciding on the operational status, and informing the operator of the process status in order to warn of an abnormal operation in advance. As the traditional system, operating based on threshold limits, separately monitors and manages each operating variable, the interaction/co-relationship among the variables is ignored. The proposed early warning system combines operating variables that interact with one another for each unit process or unit facility, producing a neural network model predicting the normal status values and generating warnings of abnormalities in the process in advance. A time extension function-linkage associative neural network model was designed and used taking consideration of the time lag. Based on the emergency advisory database established, an emergency advisory system was also developed that informs the operators of the cause, effect and emergency measures regarding abnormal operations recognized by the early warning system. The developed system was applied to the power plant operations, and it shows a good performance in early warning generation and provides good advice for the management of diagnosed abnormal situations.