The purpose of this work is to investigate the process of grammatical acquisition from data. We are using a computational learning systern that is composed of a Universal Grammar with associated parameters, and a learning algorithm, following the Principles and Parameters Theory. The Universal Grammar is implemented as a Unification-Based Generalised Categorial Grammar, embedded in a default inheritance network of lexical types. The learning algorithm receives input from a corpus annotated with

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... logical forms and sets the parameters based on this input. This framework is used as basis to investigate several aspects of language acquisition. In this paper we are concentrating on the acquisition of word order for different learners. The results obtained show the different learners having a similar performance and converging towards the target grammar given the input data available, regardless of their starting points. It also shows how the amount of noise present in the input data affects the speed of convergence of the learners towards the target.