In this paper we develop adaptive techniques for mapping generic user interfaces to synthesis engines. Upon selecting a subset of synthesis parameters, the system automatically finds the parameters-to-sound deterministic relationship in a multidimensional space. We analyze this sonic space using two different unsupervised dimensionality reduction techniques and we build the mapping using statistical information on a lower, but maximally representative, number of dimensions. The result is an adaptation of any generalpurpose interface to a specific synthesis engine, providing control directly over the perceptual features with greatest variance. This approach guarantees a linear relationship between control signals and perceptual features, and at the same time, reduces the control space dimensionality maintaining the maximum explorability of the sonic space.