Abstract 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, 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 based on a simulated continuous stirred tank reactor that exhibits multiple steady states in the outputs.