Statistical modeling of signal/image data has been used extensively for recognition and estimation. Principal component analysis was very popular for the statistical signal modeling and analysis. In this paper, we present a system to build a 3D statistical head model from incomplete data. In this system, we first transformed the 3D head scan data points into a cylindrical coordinate to obtain 2D surface maps. After these 2D surface maps were aligned, we computed the associated mean vector and covariance matrix. Then, the principal component analysis technique was applied to compute the principal components and the corresponding eigenvalues of the covariance matrix. Experimental results are given to show the 3D head shape variations from the computed 3D statistical model.