Using neural network approach, the diagnosis of faults in industrial process that requires observing multiple data simultaneously are studied. Two-stage diagnosis is proposed to analyze system faults. By using neural network, the first stage detects the dynamic trend of each normalized date patterns by comparing a proposed pattern. Instead of using neural network, the difference between stored fault pattern and real time data is used for fault diagnosis in the second stage. This method reduces the amount of calculation and saves storing space. Also, we dealt with unknown faults by normalizing the data and calculating the difference between the value of steady state and the data in case of fault. A model of tank reactor is given to verify that the proposed method is useful and effective to noise.