In IC fabrication, standard process control charts for defects often sound many false alarms, i.e. the chart incorrectly indicates that the process is out of control. It is pointed out that when non-Poisson behavior is encountered in defect data, it is necessary to determine whether this is due to an out-of-control manufacturing process or a manufacturing or data collection procedure that yields clustered defect counts. A procedure that includes the outlier removal method to discriminate between clustered defect data and a process that is out of control is proposed, and its application to two sets of real-world data is shown. One is an example from IC manufacturing where true clustering exists, and the other is a manufacturing example where persistent out-of-control conditions give the appearance of clustering. Simulation results indicate that the procedure works well within reasonable boundaries. A method for constructing defect control charts for processes that yield clustered defects is also presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>