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Fault Detection is important in many industries to ensure safe and efficient operation of a process.Actuator faults, sensor faults and process faults are some of the common faults occurring in chemical processes. To identify and control these types of faults in the system Fault detection techniques are proposed. In this present work Sensor and Process faults of shell and tube heat exchanger is detected and controlled using Artificial Neural Network (ANN) via Scaled Conjugate Gradient algorithm (SCG).A set of residuals need to be determined in order achieve fault detection.Residual indicates the state of the system and provide information about the source of possible faults. A fault detection scheme using ANN approach is proposed which are used to generate residuals. The ANN structure chosen for the work is NARX network (Nonlinear Autoregressive with External input). Network is trained using Scaled Conjugate Gradient algorithm. Mean Square Error, Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE) is obtained which are shown in simulation results. Simulation results also show the response of the process using ANN with and without Proportional Integral Derivative (PID) controller.