Two approaches for constructing control charts for quality assurance when the observations are in the form of linguistic data are presented. Both approaches are based on fuzzy set theory and use fuzzy subsets to model the linguistic terms used to describe product quality. They differ in the interpretation of the control limits and in the procedure used to reduce the fuzzy subsets to scalars for determining the chart parameters. The results obtained with simulated data suggest that, on the basis of sensitivity to process shifts, the control charts for linguistic data perform better than conventional p control charts. The number of linguistic terms used in classifying the observations was found to influence the sensitivity of these control charts. The transformation method used to obtain the representative values and the amount of fuzziness do not seem to affect the performance of either type of control charts.
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