This paper reports on word recognition experiments using a silent speech interface based on magnetic sensing of articulator movements. A magnetic field was generated by permanent magnet pellets fixed to relevant speech articulators. Magnetic field sensors mounted on a wearable frame measured the fluctuations of the magnetic field during speech articulation. These sensor data were used in place of conventional acoustic features for the training of hidden Markov models. Both small vocabulary isolated word recognition and connected digit recognition experiments are presented. Their results demonstrate the ability of the system to capture phonetic detail at a level that is surprising for a device without any direct access to voicing information