  In some real world situations, linear models are not sufficient to represent
accurately complex relations between input variables and output variables of a
studied system. Multilayer Perceptrons are one of the most successful
non-linear regression tool but they are unfortunately restricted to inputs and
outputs that belong to a normed vector space. In this chapter, we propose a
general recoding method that allows to use symbolic data both as inputs and
outputs to Multilayer Perceptrons. The recoding is quite simple to implement
and yet provides a flexible framework that allows to deal with almost all
practical cases. The proposed method is illustrated on a real world data set.
