Check-all-that-apply (CATA) has become a popular method for obtaining a consumer-based sensory characterization. In most case studies, consumers are also asked to evaluate the set of products according to a liking scale with the aim to identify the key sensory attributes associated with the most liked, or disliked, products. The common approach consists, first, in the identification of consumer segments based on the preference profiles. Thereafter, the analysis of the CATA responses is performed within each segment. Our purpose herein is to investigate different ways to simultaneously identify clusters of preference profiles while taking into account the CATA attributes. These approaches are derived from strategies already proposed by the different co-authors, namely: Fuzzy Clusterwise Regression (FCR), Clustering around Latent Variables (CLV) approach with external data, CLUSCATA-liking and CLV3W. The first two approaches involve the aggregation of the individual CATA data into a contingency table, while the last two ones deal with the combination of liking and CATA data at the individual level. These four strategies are illustrated on the basis of a real case study. Results are compared with respect to cluster stability together with interpretability of liking profiles within each segment. The stability of the results, assessed by bootstrapping, differed according to the strategy used. Moreover, working at the individual level or with combined data lead to a somewhat different segmentation of the panel of consumers.