The topic of this chapter is forecasting with nonlinear models. First, a number of well-known nonlinear models are introduced and their properties discussed. These include the smooth transition regression model, the switching regression model whose univariate counterpart is called threshold autoregressive model, the Markov-switching or hidden Markov regression model, the artificial neural network model, and a couple of other models. Many of these nonlinear models nest a linear model. For this reason, it is advisable to test linearity before estimating the nonlinear model one thinks will fit the data. A number of linearity tests are discussed. These form a part of model specification: the remaining steps of nonlinear model building are parameter estimation and evaluation that are also briefly considered. There are two possibilities of generating forecasts from nonlinear models. Sometimes it is possible to use analytical formulas as in linear models. In many other cases, however, forecasts more than one periods ahead have to be generated numerically. Methods for doing that are presented and compared. The accuracy of point forecasts can be compared using various criteria and statistical tests. Some of these tests have the property that they are not applicable when one of the two models under comparison nests the other one. Tests that have been developed in order to work in this situation are described. The chapter also contains a simulation study showing how, in some situations, forecasts from a correctly specified nonlinear model may be inferior to ones from a certain linear model. There exist relatively large studies in which the forecasting performance of nonlinear models is compared with that of linear models using actual macroeconomic series. Main features of some such studies are briefly presented and lessons from them described. In general, no dominant nonlinear (or linear) model has emerged.