Abstract : This work deals with non linear dynamical system identification. A multiple model architecture which overcomes certain insufficiencies of MLP neural networks is studied. Multiple model approach consists of modeling complex systems by mean of a set of simple local models whose validity are limited in well defined zones. Instead of using conventional affine models, a more general polynomial structure is proposed in this study, enabling to better apprehend local non linearities, reducing thus the number of local models.Models parameters of such a structure are estimated by linear optimization, which reduces computation time with respect to parameter estimation of a neural network architecture. The implementation of recurrent multiple models, with a more convenient learning algorithm than the back propagation through time, used in recurrent MLP models, is also studied. Such implementations facilitate representation of recurrent models like NARMAX and NOE. The determination of the number of local models in a multiple model architecture requires decomposition of system's feature space into several sub-systems in which local models are defined. Fuzzy partitioning methods (based of « fuzzy-c-means », « Gustafson and Kessel » and « subtractive clustering »algorithms) are presented. The use of such methods requires the implementation of a multiple model architecture where local models can have different structures : polynomial with different degrees, neural or polynomial and neural. A multiple model with a heterogeneous architecture satisfying these requirements is proposed and structural and parametrical identification algorithms are presented. A comparative study between multiple model and MLP architectures is done. The main advantage of the multiple model architecture is the parameter estimation simplicity. In addition, the use of fuzzy partitioning methods in multiple model architecture enables to find easily the number of local models while the determination of hidden neurons in an MLP architecture remains a hard task.