Multiscale signal decomposition represents an important step to enhance process monitoring results in many manufacturing applications. Empirical mode decomposition (EMD) is a data driven technique that gained an increasing interest in this framework. However, it usually yields an-over decomposition of the signal, leading to the generation of spurious and meaningless modes and the possible mixing of embedded modes. This study proposes an enhanced signal decomposition approach that synthetizes the original information content into a minimal number of relevant modes via a data-driven and automated procedure. A criterion based on the kernel estimation of density functions is proposed to estimate the dissimilarities between the intrinsic modes generated by the EMD, together with a methodology to automatically determine the optimal number of final modes. The performances of the method are demonstrated by means of simulated signals and real industrial data from a waterjet cutting application.