A new stage concept was developed to reliably identify counterfeit tablets which are very similar to the genuine drug product. This concept combines single-point near-infrared spectroscopy (NIRS) and near-infrared chemical imaging (NIR-CI) with statistical variance analysis. The advantage of NIR-CI over NIRS is the potential to determine not only the amount, but also the spatial distribution of ingredients within a single tablet. Previously published NIR-CI studies used homogeneity as a key indicator for the identification of counterfeits. The state of the art approach for estimating homogeneity is to record the average and % standard deviation of predicted classification scores (i.e. concentrations) for a given component within a specimen. A disadvantage of this approach is the partial loss of spatial information. In view of this, we developed a new method using much more of the spatial information for the estimation of homogeneity. The method is based on (1) summation and unfolding of multidimensional predicted classification scores, which results in a Linear Image Signature (LIS) and (2) multivariate LIS data analysis (LIS-MVA). It could be demonstrated that this kind of NIR-CI data analysis represents an innovative approach for the identification of counterfeit tablets. Moreover, this procedure is applicable to determine the product variability, i.e. process signature of a given product thus being a valuable tool within the Quality by Design (QbD) approach of the ICH Q8 guideline.