Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. One could build a so-called white-box model based on first principles, eg. a model for a physical process from the Newton equations, but in many cases such models will be overly complex and possibly even impossible to obtain in reasonable time due to the complex nature of many systems and processes. In the context of non-linear model identification Jin et al. describe greybox modeling as assuming a model structure a priori and then estimating the model parameters. This model structure can be specialized or more general so that it is applicable to a larger range of systems or devices. The parameter estimation is the tricky part and Jin et al. point out that the search for a good fit to experimental data tend to lead to an increasingly complex model. Jin et al. then define a black-box model as a model which is very general and thus containing little a priori information on the problem at hand and at the same time being combined with an efficient method for parameter estimation.