The simultaneous analysis of several data matrices related to the same set of samples can be done with the use of multiblock methods. Common components and specific weights analysis (CCSWA), also called ComDim, is one method enabling the analysis of such data. CCSWA, which is a multiblock version of principal components analysis (PCA), is particularly interesting as it can easily be modified and transformed, either by replacing its PCA-based data decomposition by another multivariate method, such as independent components analysis or partial least-squares regression, for example, or by segmenting the data matrices before multiblock analysis, or both. These modifications aim at improving the models and their interpretation. This article will give a concise description of ComDim and then a presentation of several recent extensions of the method, showing its usefulness as both a supervised and a non-supervised method.