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Sociological literature on cultural practices seeking to understand the social differentiation of taste pays limited attention to what people avoid consuming, despite its potential as a strategic indicator of taste. Avoidance has special relevance for the understanding of eating and drinking practices which are often characterized by exclusion of items for health, hedonic, reputational, or spiritual reasons. Making use of rich data on twenty-three items commonly consumed by Italian adults, this paper investigates how avoidances—i.e. what people claim never to eat or drink—are clustered, socially patterned and have evolved over time. Methodologically, we propose the novel use and integration of two machine learning techniques—Self-Organizing Maps (SOM) and Boosted Regression Trees (BRT)— to identify nine highly homogeneous avoidance clusters and examine the power of social variables in predicting the probability of individuals' belonging to various clusters and to further characterize them. We conclude by discussing possible rationales behind avoidance. [ABSTRACT FROM AUTHOR]