Time and frequency localizations are of crucial importance in the analysis of nonlinear and non-stationary processes, especially in systems with high level of complexity where detection of information/events, estimation of parameters and classification of signals in classes is necessary to take decisions. The Hilbert Huang Transform (HHT) offers an adaptive approach to analyze no-linear and non-stationary processes. This paper exposes the HHT approach and its new methodologies for improvement of the analysis, such as the masking process. Two examples are given to show the techniques, first a synthetic signal, representing a typical behavior of an electrical signal immersed in a power electronic environment and second a brain signal to extend the acknowledgment to a biological process. Finally a mode mixing separation technique is presented.