Mathematical models that describe the transport phenomena and quality changes of foods during processing contain several uncertain model input parameters which result in uncertain model predictions. The objective of this study was to evaluate the impact of uncertain input parameters on the model predictions of the mechanistic 3D model of chicken meat roasting and to identify as well as rank the most important model parameters. We found that the uncertainty in the model output variables varies with roasting time, but also among the different output variables, by using the Monte Carlo method. To decompose the variance with respect to the input parameters, the method of standardized regression coefficients (SRC), a global sensitivity analysis method, was used. Consequently, the uncertain input parameters were ranked according to their relative impact. The results of the SRC method were compared with the Morris screening, a one-step-at-a-time (OAT) global sensitivity analysis method. The comparison of the two applied sensitivity methods showed that the ranking of the input parameters is similar, while the Morris screening is more efficient computational wise. Finally, we illustrate how the results of the analyses can be used for model refinement as well as to highlight parameters and areas where further research is necessary.