Knee-joint sounds could potentially be used to noninvasively probe the physical and/or physiological changes in the knee associated with rehabilitation following acute injury. In this paper, a system and methods for investigating the consistency of knee-joint sounds during complex motions in silent and loud background settings are presented. The wearable hardware component of the system consists of a microelectromechanical systems microphone and inertial rate sensors interfaced with a field programmable gate array-based real-time processor to capture knee-joint sound and angle information during three types of motion: flexion-extension (FE), sit-to-stand (SS), and walking (W) tasks. The data were post-processed to extract high-frequency and short-duration joint sounds (clicks) with particular waveform signatures. Such clicks were extracted in the presence of three different sources of interference: background, stepping, and rubbing noise. A histogram-vector Vn(→) was generated from the clicks in a motion-cycle n, where the bin range was 10°. The Euclidean distance between a vector and the arithmetic mean Vav(→) of all vectors in a recording normalized by the Vav(→) is used as a consistency metric dn. Measurements from eight healthy subjects performing FE, SS, and W show that the mean (of mean) consistency metric for all subjects during SS (μ [ μ (dn)] = 0.72 in silent, 0.85 in loud) is smaller compared with the FE (μ [ μ (dn)] = 1.02 in silent, 0.95 in loud) and W ( μ [ μ (dn)] = 0.94 in silent, 0.97 in loud) exercises, thereby implying more consistent click-generation during SS compared with the FE and W. Knee-joint sounds from one subject performing FE during five consecutive work-days (μ [ μ (dn) = 0.72) and five different times of a day (μ [ μ (dn) = 0.73) suggests high consistency of the clicks on different days and throughout a day. This work represents the first time, to the best of our knowledge, that joint sound consistency has been quantified in ambulatory subjects performing every-day activities (e.g., SS, walking). Moreover, it is demonstrated that noise inherent with joint-sound recordings during complex motions in uncontrolled settings does not prevent joint-sound-features from being detected successfully.