The objective of this thesis is to create a new type-2 fuzzy inference system for the treatment of uncertainties with automatic learning and that provides an interval of confidence for its defuzzified output through the calculation of corresponding type-reduced sets. In order to attain this objective, this new model combines the paradigms of the modelling of the type-2 fuzzy inference systems and neural networks with techniques of recursive BSP partitioning. This model mainly has the capacity to model and to manipulate most of the types of existing uncertainties in real situations, diminishing the effects of these to produce a better performance. In addition, it has the independent capacity to create and to expand its own structure automatically, to reduce the limitation referred to the number of inputs and to extract rules of knowledge from a data set. This new model provides a confidence interval, that constitutes an important information for real applications. In this context, this model surpasses the limitations of the type-2 fuzzy inference systems - complexity computational, small number of inputs allowed and limited form, or nonexistent, to create its own structure and rules - and of the type-1 fuzzy inference systems - incomplete adaptation to uncertainties and not to give an interval of confidence for the output. The type-1 fuzzy inference systems also present limitations with regard to the small number of inputs allowed, but the use of recursive partitioning, already explored with excellent results [SOUZ99], reduce significantly these limitations. This work constitutes fundamentally of four parts: a study on the different existing type-2 fuzzy inference systems, analysis of the hierarchical neuro- fuzzy systems that use type-1 fuzzy sets, modelling and implementation of the new type-2 hierarchical neuro-fuzzy BSP model and study of cases. The new model, denominated type-2 hierarchical neuro-fuzzy BSP model (T2-HNFB) was defined from the study of the desirable characteristics and the limitations of the type-2 and type-1 fuzzy inference systems and the existing hierarchical neuro-fuzzy systems that use type- 1 fuzzy sets. Of this form, the T2-HNFB model is modelling and implemented with the attributes of interpretability and autonomy, from the conception of type-2 fuzzy inference systems, neural networks and recursive BSP partitioning. The developed model is evaluated in different benchmark databases and real applications of forecast and approximation of functions. Comparisons with other models are done. The results obtained show that T2-HNFB model provides, in forecast and approximation of functions, next results and in several cases superior to the best results provided by the models used for comparison. In terms of computational time, its performance also is very good. In forecast and approximation of functions, the intervals of confidence obtained for the defuzzified outputs are always coherent and offer greater credibility in most of cases when compared with intervals of confidence obtained through traditional methods using the forecast outputs by the other models and the own T2-HNFB model.