As a means of effectively managing severe accidents at nuclear power plants, it is important to identify and diagnose accident initiating events within a short time interval after the accidents by observing the major measured signals. The main objective of this study was to diagnose loss of coolant accidents (LOCAs) using artificial intelligence techniques, such as SVC (support vector classification) and GMDH (group method of data handling). In this study, the methodologies of SVC and GMDH models were utilized to discover the break location and estimate the break size of the LOCA, respectively. The 300 accident simulation data (based on MAAP4) were used to develop the SVC and GMDH models, and the 33 test data sets were used to independently confirm whether or not the SVC and GMDH models work well. The measured signals from the reactor coolant system, steam generators, and containment at a nuclear power plant were used as inputs to the models, and the 60 sec time-integrated values of the input signals were used as inputs into the SVC and GMDH models. The simulation results confirmed that the proposed SVC model can identify the break location and the proposed GMDH models can estimate the break size accurately. In addition, even if the measurement errors exist and safety systems actuate, the proposed SVC and GMDH models can discover the break locations without a misclassification and accurately estimate the break size.