Statistical process control techniques have been used to detect any assignable cause that may result in a lower quality. Among these techniques is the identification of any abnormal patterns that may indicate the presence of an assignable cause. These abnormal patterns may be in the form of steady movement in one direction, i.e., trends; an instantaneous change in the process mean, i.e., sudden shift; a series of high observations followed by a series of low observations, i.e., cycles. As long as we can classify the observed data the decision maker can decide on actions to be performed to ensure quality standards and planning for interventions. In identification of these abnormal patterns, rather than relying on human element, intelligent tools have been proposed in the literature. We attempt to provide a comparative study of various classification algorithms used for pattern identification in statistical process control. We specifically consider six different types of patterns to classify. These different types are: (1) Normal, (2) Upward trend, (3) Downward trend, (4) Upward shift, (5) Downward shift, (6) Cyclic. A recent trend in classification is to use deep neural networks (DNNs). However, due to the design complexity of DNNs, alternative classification methods should also be considered. Our focus on this study is to compare traditional classification methods to a recent DNN solution in the literature in terms of their efficiencies. Our numerical study indicates that basic classification algorithms perform relatively well in addition to their structural advantages.