Sensitivity analysis of uncertain nonlinear systems plays an important role in analyzing chemical engineering process models. Even though a variety of different techniques exists for analyzing nonlinear systems, these methods often do not update the parameter distribution using available measurement data. This forms the motivation behind this work as a sensitivity analysis procedure is introduced that deals with nonlinear systems and uncertainty in model parameters, and also incorporates the use of available data. The technique computes two covariance matrices: a covariance matrix that describes the effect that changes of the parameters values, sampled from a prior distribution, have on the outputs, and a covariance matrix that determines the effect of available data on the posterior distribution of the parameters using a Markov chain Monte Carlo method. The information contained in these two covariance matrices is then simultaneously evaluated by balancing the two matrices. The results returned by this technique are the directions in parameter space that have the largest impact on the system behavior according to the given parameter uncertainty as well as the contribution of individual parameters to these important directions in parameter space. The presented technique is applied to three examples.