Recently, various types of hand shape recognition systems have been developed for human–machine interfaces. However, most wearable recognition systems cannot robustly handle the variations in attachment positions of the devices. Thus, we propose a hand shape recognition system using a wearable multi-joint wrist contour measuring device to realize robust and effective recognition of hand shape, regardless of attachment position variations. In particular, this device can measure the wrist contour and band flexion data to recognize hand shape. The wrist contour data are measured using photo-reflectors mounted inside the device, and the band flexion data are measured using photo-interrupters installed at the device joints. Additionally, the attachment position information is extracted from the wrist contour or band flexion data using dimensional compression or attachment position recognition to achieve robustness against the position variations. Subsequently, the extracted information is incorporated into the hand shape recognizer. The results of the recognition experiment demonstrate that the attachment position information extracted from the band flexion data using dimensional compression could effectively realize robust hand shape recognition, considering variations in the device attachment position.