Identification of nonlinear systems is a very extensive problem, with roots and branches in several diverse fields. It is not possible to survey the area in a short text. The current presentation gives a subjective view on some essential features in the area. These concern a classification of methods, the use of different shades of grey in models, and some overall issues like bias-variance trade-offs, data sparseness and the peril of local minima.