Process fault detection and diagnosis is an important problem in plant control at the supervisory level. It is the central component of abnormal event management which has attracted a lot of attention recently. In this study, the use of artificial neural networks (ANN) for fault detection is explored. An ANN can represent nonlinear and complex relations between its inputs (sensor measurements) and outputs (faults). As a test case, absorption of CO2 gas in monoethanolamine (MEA) by a pilot plant called "automatic absorption and stripping pilot plant" is studied. For detecting and diagnosis of faults, variations in feed rate, feed composition, liquid absorber rate and composition are imposed onto the plant. The faults in this process influence variables such as the composition of absorbed gas (CO2) and temperature and pressure drop of the column. The CO2 concentration in the product should not exceed a certain limit. By selecting a proper architecture for the network (5-9-10), it is possible to detect the faults accurately. The network is trained using the back propagation method. The developed fault diagnosis algorithm is tested using data that has not been seen by the network.