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We design a neural network model of first language acquisition to explore the relationship between child and adult speech sounds. The model learns simple vowel categories using a produce-andperceive babbling algorithm in addition to listening to ambient speech. The model is similar to that of Westermann & Miranda (2004), but adds a dynamic aspect in that it adapts in both the articulatory and acoustic domains to changes in the childs speech patterns. The training data is designed to replicate infant speech sounds and articulatory configurations. By exploring a range of articulatory and acoustic dimensions, we see how the child might learn to draw correspondences between his or her own speech and that of a caretaker, whose productions are quite different from the childs. We also design an imitation evaluation paradigm that gives insight into the strengths and weaknesses of the model.