Abstract: Sensory evaluation plays a remarkable role in maintaining the quality standards of beverages such as tea. The quality, which determines the price of tea, is evaluated by professional tea tasters. Uncertainty and vagueness of sensory evaluation has been a serious issue in selection of good quality tea. An issue existing when analyzing sensory data to detect panel disagreement is that data of three dimensional (three–way) or higher are often reduced to two–way data. Present study aimed to investigate the possibility of using Clustering around Latent Variables for three–way data (CLV3W) method to detect panel disagreement in sensory data of Sri Lankan tea.