In this paper, an integrated approach to fast and efficient construction of statistical shape models is proposed that is a potentially useful tool in Information Retrieval(IR). The tool allows intuitive extraction of accurate contour examples from a set of images using a semi-automatic segmentation approach. The user is allowed to draw on the scene by simply dragging a mouse over the image and creating a set of labelled scribbles for the objects to be segmented. An automatic segmentation algorithm uses the scribbles to partition the scene and extract objects’ contour. A set of labelled points (landmarks) is identified automatically on the set of examples thereby allowing statistical modeling of the objects’ shape. The main contribution of this paper is the new approach to automatic landmark identification eliminating the burden of manual landmarking. The approach utilizes a robust method for pairwise correspondence proposed originally in [1, 2]. The landmarks are used to train statistical shape models known as Point Distribution Models (PDM) [11]. Qualitative results are presented for 3 classes of shape which exhibit various types of nonrigid deformation.