Traditionally, the Parkinson disease is diagnosed and followed up by conventional clinical tests that are fully dependent on the expert experience. The diffuse boundary between normal and early Parkinson stages and the high variability of gait patterns difficult any objective characterization of this disease. An automatic characterization of the disease is herein proposed by mixing up different measures of the ipsilateral coordination and spatiotemporal gait patterns which are then classified with a classical support vector machine. The strategy was evaluated in a population with Parkinson and healthy control subjects, obtaining an average accuracy of 87% for the task of classification.