This chapter investigates the Free Comment (FC) method as a tool for sensory and hedonic characterization of products, emphasizing its unique advantage of enabling participants to express perceptions using their own language. Unlike structured approaches such as Check-All-That-Apply (CATA), FC avoids biases linked to pre-established descriptor lists. The chapter synthesizes current academic state of art and practitioner usage of FC, based on a survey among practitioners belonging to French and European sensory science societies. Key findings highlight diverse applications, benefits (e.g., spontaneous, rich data), and challenges, notably in comment preprocessing and statistical analysis. Manual coding remains common, though automated approaches using NLP or LLMs are emerging. Statistically, the chapter advocates for the use of the multiple-response chi-square framework, more suited to FC data structure than traditional methods. Advanced analyses such as sensory mapping, preference mapping, and regression-based approaches to linking liking scores with sensory descriptors derived from free comments are detailed. A case study on madeleines illustrates practical applications. Ultimately, the chapter underscores the need for reproducible preprocessing pipelines and accessible statistical tools to bridge gaps between academic methodologies and practitioners’ practices.