A potential multivariate statistical based acoustic signal analysis and processing technique is presented for dynamic knee assessment for the smaller dataset. By using the integrated data acquisition system developed by the authors from two age-matched elder groups, the dynamic acoustic and the corresponding joint angle signals emitted from the consecutive knee movements are acquired. Consecutive movement cycles are isolated into individual for further analysis, and the cumulative probability distribution is employed for feature selection.Multivariate statistic methodologies are employed to derive the acoustic emission based joint profiles and to create the visual effect among the healthy and osteoarthritic groups, as well as to create the cluster evidence to demonstrate the feasibility of diagnosing knee osteoarthritis using the dynamic acoustics emission. The research findings show not only the potentials for simplifying the data acquisition protocol, but also the discovery of movement significancy.