This paper investigates the computational grounding of learning theories developed within a metrical phonology approach to stress assignment. In current research the Principles and Parameters approach to learning stress is pervasive. W e point out some inherent problems associated with this approach in leaxning the stress sys- tem of Dutch. T h e paper focuses on two specific aspects of the learning task: w e empirically investigate the eflFect of input encodings on learnability, and w e exam- ine the possibility of a data-oriented ap- proach as an alternative to the Principles and Parameters approach. W e show that a data-oriented similarity-based machine learning technique (Instance-Based Learn- ing), working on phonemic input encodings is able to learn metrical phonology abstrac- tions based on concepts like syllable weight, and that in addition, it is able to extract generalizations which cannot be expressed within a metrical framework.