In this paper we try to propose an automated method for cobb angle computation from radiograph (x- ray) images of scoliosis patients where the objective is to have increased reliability of spinal curvature quantification. The automatic technique mainly comprises of four steps, namely: pre-processing (denoising and filtering), region of interest (roi) identification, feature extraction and cobb angle computation from the extracted spine centre-line.svm (support vector machine) classifier is used for object identification and feature extraction. It is assumed that spine is a continuous structure instead of a series of discrete vertebral bodies with individual orientations. Several methods are used for the identification of centre-line of spine: morphological operation, gaussian blurring and polynomial fit. Now tangents are taken at every point of the extracted centre-line and thus we can evaluate the cobb angle from these sets of tangents. For the analysis of the automated diagnosis process, the approach was evaluated on the basis of 25 coronal x-ray images. Region of interest identification which is based on svm classifier is effective enough at a specificity of 100% and approximately 58% results in the extraction of centre-line from this roi were accurate where the angular variability is very less or negligible. Due to poor radiation dose and several other reasons, the endplates and edges of spine in radiograph images were blurred and hence the continuous contour based approach gives better reliability.