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In This work we present an approach using incrementally constructed neural gas networks to 'grow' an intuitive interface for interactive exploratory sonification of high-dimensional data. The sonifications portray information about the intrinsic data dimensionality and its variation within the data space. The interface follows the paradigm of model-based sonification and consists of a graph of nodes that can be acoustically 'excited' with simple mouse actions. The sound generation process is defined in terms of the node parameters and the graph topology, following a physically motivated model of energy flow through the graph structure. The resulting sonification model is tied to the given data set by constructing both graph topology and node parameters by an adaptive, fully data-driven learning process, using a growing neural gas network. We report several examples of applying this method to static data sets and point out a generalization to the task of process analysis.