A method for assessing the uncertainty of the individual bilinear model parameters from two-block regression modelling by multivariate partial least squares regression (PLSR) is presented. The method is based on the so-called “Jack-knife” resampling, comparing the perturbed model parameter estimates from cross-validation with the estimates from the full model. The conventional jack-knifing from ordinary least squares regression is modified in order to compensate for rotational ambiguities of bilinear modelling. The method is intended to make “do-it-yourself” multivariate data-analysis by non-statisticians more safe, in particular in cases with many collinear and noisy regressor and -regressand variables (which is very common in practice). Its use is illustrated by a real example, where the chemical and physical properties of different cocoa drinks are predicted from sensory analysis.