Statistical process control typically involves monitoring of control charts to detect anormal patterns and perform corrective operations upon detection of an anormality in the system. Although control charts could be utilized to determine if a process is in-contol or out-of-control, they may not be useful for ordinary and less-experienced users due to their complexity and difficulty of interpretation. Thus, an automated system which could monitor the process, detect out-of-control situations, and recommend corrective operations is highly desired. The quality of a product or process, in some statistical process cotrol methods, is characterized by a relationship between a response variable and one or more explanatory variables, called profile . Profile monitoring is used to understand and check the stability of this relationship over time. While a profile could be as simple as a linear regression model, for exapmle in calibration, a more complex model may be needed for some applications. In this project, we investigate the application of Artificial Neural Networks for monitoring of generalized linear regression profiles. The aim is to monitor a profile and detect out-of-control situations with minimal delay. A feed-forward multilayer perceptron artificial neural network is applied to monitor binary and possion profiles. The performance of the proposed method is evaluated using simulation and numerical examples. The results are compared to those obtained using T 2 control charts. It is shown that the proposed method could detect the shifts in the parameters of binary profiles when the process is out-of-control.