Empirical modelling algorithms build mathematical models of systems based on observed data. In this paper we present an analysis of the ASMOD algorithm. ASMOD uses B-splines to represent general nonlinear models of several variables. The internal structure of the model is, through an incremental refinement procedure, automatically adapted to the dependencies observed in the data. We derive, using risk minimization theory, uniform upper and lower bounds on the expected value of the criterion function used for model estimation. The bounds are given in terms of the empirical value of the criterion function computed on a finite number of data points. We also analyse the asymptotic convergence properties of the algorithm, and under natural conditions we show convergence to a model which minimizes the expected value of the estimation criterion.