ABSTRACT Statistical process control charts aim  to detect out-of-control signals which indicate existence of special causes effecting the process. Once such a signal is detected, the interpretation of the signal, that is, discovering the actual causes behind the signal, rests upon the shoulders  of operators or engineers. Recently, some techniques have been developed  for making this interpretation process easier. This study presents an overview of such techniques in three categories: traditional one-variable control charts, artificial neural network applications and multivariate control charts.   Key Words: SPC  charts, out-of-control signal,  problem diagnosis