The pattern recognition approach to expanding the usefulness and effectiveness of traditional control charts has been proposed and studied. The approaches adopted vary from statistical-based or artificial-intelligence-based (e.g. expert systems), to computational-intelligence-based (e.g. neural networks), or a mixture. Although general-purpose control chart pattern recognition systems have been shown to be useful in identifying a variety of non-random patterns, this was achieved at the price of losing the ability to identify certain details in individual pattern classes. Therefore, a special-purpose system is desirable to compensate for the limitation of a general-purpose system. In this research, a number of special-purpose (for cyclic data) control chart pattern recognizers based on several neural-network paradigms—namely back-propagation, ART1, ARTMAP, and fuzzy ARTMAP—were developed and their performance was carefully studied and compared. Extensive simulation was conducted to study: the use of proper training data and training strategies, the effect of complement coding, the use of binary or analogue input, and the proper range of pattern parameter values. The performance was measured by type I and type II errors as well as by average run length. In general, a special-purpose system shows significant improvement over its corresponding general-purpose system. Although back-propagation systems are more tolerant of a high level of noise, the family of ART-based systems performs more homogeneously across the range of the cycle amplitude studied. Within the ART family, fuzzy ARTMAP with complement coding outperforms other ART-based systems.