This paper discusses the development and evaluation of SenseHub, a web application based on R statistical language, which is specially tailored to answer unprecedented growth in sensory science. Design of experiment, analyses for panel performance, and analyses for twenty-one sensory methods (covering discriminative, descriptive, a ective, and optimisation methods) are available in SenseHub. A total of 35 subjects participated in evaluating the performance of SenseHub versus another statistical software on analysing a sensory data. The participants were instructed to perform analysis on an example dataset using SenseHub and Minitab in randomised order. The example dataset was obtained from another study utilising QDA method on 12 perfumes evaluated by 103 panellists using 21 sensory descriptors. In this study, the participants were asked to answer ten questions about data interpretation followed by 11 questions about satisfaction. Time spent for analysis was recorded. Results show that the participants could answer 87.06% questions correctly when using SenseHub while only 76.25% correct answers were achieved when using Minitab (|t|44.94=2.36; P<0.05). SenseHub also has higher average satisfaction scores than Minitab in all 11 criteria (two sample t-test, P=5.68x10-9 – 2.25x10-2). The average satisfaction scores of SenseHub ranged from 84.57 (user interface) to 90.34 (suitability) while the scores of Minitab ranged from 58.20 (understandable) to 69.49 (data import). Finally, the average time spent using SenseHub is nearly half-time quicker than that of Minitab, 11.53 versus 19.73 minutes (|t|53.27=3.61; P<0.05). In short, SenseHub is not only equipped with powerful statistical tools but also taking user experience into consideration thus make it promising as an application for sensory analyses.