The increasing availability of data sets with a huge amount of information, coded in many diff erent features, justifi es the research on new methods of knowledge extraction: the great challenge is the translation of the raw data into useful information that can be used to improve decisionmaking processes, detect relevant profi les, fi nd out relationships among features, etc. It is undoubtedly true that a picture is worth a thousand words, what makes visualization methods be likely the most appealing and one of the most relevant kinds of knowledge extration methods. At ESANN 2011, the special session "Seeing is believing: The importance of visualization in real-world machine learning applications" reflects some of the main emerging topics in the field. This tutorial prefaces the session, summarizing some of its contributions, while also providing some clues to the current state and the near future of visualization methods within the framework of Machine Learning.