Multivariable additive NARX (nonlinear autoregressive with exogenous inputs) modeling of process systems is presented. The model structure is similar to that of a generalized additive model (GAM) and is estimated with a nonlinear canonical variate analysis (CVA) algorithm called CANALS. The system is modeled by partitioning the data into two groups of variables. The first is a collection of future outputs, and the second is a collection of past input and outputs and future inputs. This approach is similar to linear subspace state-space modeling. An illustrative example of modeling is presented on the basis of a simulated continuous chemical reactor that exhibits multiple steady states in the outputs for a fixed level of the input.