We report work on mapping the acoustic speech signal, parametrized using Mel Frequency Cepstral Analysis, onto electromagnetic articulography trajectories from the MOCHA database. We employ the machine learning technique of Support Vector Regression, contrasting previous works that applied Neural Networks to the same task. Our results are comparable to those older attempts, even though, due to training time considerations, we use a much smaller training set, derived by means of clustering the acoustic data.