Internal models are neural mechanisms that can mimic the input-output properties of controlled objects. Our studies have shown that: 1) an internal model for a novel tool is acquired in the cerebellum (Imamizu et al., 2000); 2) internal models are modularly organized in the cerebellum (Imamizu et al., 2003); 3) their outputs are sent to the premotor regions after learning (Tamada et al., 1999); and 4) the prefrontal and parietal regions contribute to the blending of the outputs (Imamizu et al., 2004). Here, we investigated changes in global neural networks resulting from the acquisition of a new internal model. Human subjects manipulated three types of rotating joystick whose cursor appeared at a position rotated 60°, 110°, or 160° around the screen's center. In a pre-test after long-term training (5 days) for the 60° and 160° joysticks, brain activation was scanned during manipulation of the three joysticks. The subjects were then trained for the 110° for only 25 min. In a post-test, activation was scanned using the same method as the pre-test. Comparisons of the post-test to the pre-test revealed that the volume of activation decreased in most of the regions where activation for the three rotations was observed. However, there was an increase in volume at a marginally significant level (p ≤ .08) only in the inferiorlateral cerebellum and only for the 110° joystick. In the cerebral cortex, activation related to 110° decreased in the prefrontal and parietal regions but increased in the premotor and supplementary motor area (SMA) regions. These results can be explained by a model in which outputs of the 60° and 160° internal models are blended by prefrontal and parietal regions to cope with the novel 110° joystick before the 25-minute training; after the acquisition within the cerebellum of an internal model for the 110°, output is directly sent to the premotor and SMA regions, and activation in these regions increases.