BACKGROUND: Knee problems are the most common complaints of the lower extremity in the Netherlands. Osteoarthritis has the highest prevalence of all knee complaints. Knee osteoarthritis is likely to start at the patellofemoral joint and is associated with the aggravation of pain. Mechanical overloading is hypothesized to contribute to the development and progression of patellofemoral osteoarthritis (PFOA). Several studies have explored the mechanical loading pattern of the patellofemoral joint. Because different biomechanical models were used, vastly different estimations of the patellofemoral joint contact force (PFJCF) were concluded. This diversity prevents a clear understanding of the role of mechanical overloading in PFOA, since it is unknown how different biomechanical models affect the PFJCF estimation. OBJECTIVES: This study will explore how different biomechanical models of the patellofemoral joint affect the estimated PFJCF for common weight-bearing activities. METHODS: Ten healthy participants were included in this study. Common weight-bearing activities (walking, stair ascending, stair descending, sit-to-stand and stand-to-sit) were performed in the motion lab. Marker trajectory data and force plate data were collected. The data were input to biomechanical models used to estimate the quadriceps muscle force and subsequently the PFJCF. The quadriceps muscle force was estimated using the inverse dynamics and static optimization method. From there on, the PFJCF was estimated using three PFJCF to quadriceps muscle force ratios (P2QFRs), each based on a different patellofemoral joint model (i.e. van Eijden’s model, Yamaguchi’s model and Gill’s model). For each weight-bearing activity, the peak PFJCF was obtained and the magnitude of the difference among the biomechanical models was explored. RESULTS: The static optimization method resulted in a significantly higher peak PFJCF compared to the inverse dynamics method in walking (largest effect size was 0.10 BW). However, for stair descending, sit-to-stand and stand-to-sit, the inverse dynamics method resulted in a significantly higher peak PFJCF compared to the static optimization method (largest effect size was 0.45 BW, 1.17 BW, 1.25 BW, respectively). No significantly difference was found for stair ascending. For walking, Yamaguchi’s model resulted in a significantly higher peak PFJCF compared to van Eijden’s model and Gill’s model, and van Eijden’s model resulted in a significantly higher peak PFJCF compared to Gill’s model (largest effect size was 0.06 BW). For stair ascending, stair descending, sit-to-stand and stand-to-sit, Gill’s model resulted in a significantly higher peak PFJCF compared to van Eijden’s model and Yamaguchi’s model. For stair descending the van Eijden’s model resulted in a significantly higher peak PFJCF compared to Yamaguchi’s model. The largest effect size was 0.15 BW, 0.32 BW, 0.72 BW and 0.72 BW, for respectively stair ascending, stair descending, sit-to-stand and stand-to-sit. CONCLUSION: The choice of a biomechanical model has a critical effect on the estimation of the magnitude of the PFJCF. Its differences might reach half the clinical size effects when comparing control to symptomatic PF pain patients.