In this paper we give an overview and discussion of the basic steps of system identification. The four main ingredients of the process that takes us from observed data to a validated model are: (1) the data itself, (2) the set of candidate models, (3) the criterion of fit, and (4) the validation procedure. We discuss how these ingredients can be blended to a useful mix for model-building in practice.