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This paper presents a neural network based fault diagnosing approach which allows dynamic fault identification. The method utilizes the principal component analysis (PCA) technique to dramatically reduce the problem dimension. Such a dimension reduction approach leads to faster diagnosing and allows a better graphical presentation of the results. To show the effectiveness of the proposed approach, two methodologies are used to train the neural network (NN). At first, a training matrix composed of 15 variables is used to train a multilayer perceptron neural network (MLP) with resilient backpropagation (RP) algorithm. Employing the proposed method, a more accurate and simpler network is designed where the input size is reduced from 15 to 6 variables for training the NN. In short, the application of PCA highly reduces the network topology and allows employing more efficient training algorithms. The developed networks use, as input, a short set (in a moving temporal window (MTW)) of recent measurements of each variable avoiding the necessity of using starting events. The accuracy, generalization ability and reliability of the designed networks are verified using 10 simulated events data from a VVER-1000 simulator. Noise is added to the data to evaluate robustness of the method, and the method again shows to be effective and powerful.