AbstractFuzzy adaptive least squares (FALS91), a pattern recognition method for analyzing structure‐activity rating data to generate QSAR models, has been developed. A novel feature of FALS91 is that the degree to which each sample belongs to its activity class is given using a fuzzy membership function. This paper first describes the algorithm and calculation procedure of FALS91, and then shows its application to the correlation of structure with the activity rating of 31 calmodulin inhibitors and 29 α‐methylene‐γ‐butyrolactones with allergenic activity. Considerably high reliability was shown in both recognition and leave‐one‐out prediction of the FA