Segmenting a 3D model is an important challenge since this operation is relevant for many applications. It is very important in 3D image processing that the segmentation algorithm be able to find relevant and meaningful geometric primitives automatically. Here, we adapted a 2D spectral segmentation method, Power Iteration Clustering (PIC), to the case of 3D models. This method is fast and easy to implement. A similarity matrix based on normals to vertices is defined and a modified version of PIC is implemented in order to segment a 3D model. The proposed method is validated on both free-form and CAD (Computer Aided Design) models, on real data captured by handheld 3D scanners, and in the presence of noise. Results demonstrate the efficiency and robustness of the method in all cases.