Image stitching is the method of creating a composite image from several images of the same scene. This paper addresses the issues of generating a seamless panoramic image from a series of photographs of the same scene by varying scale, orientation and illumination. A feature-based approach is proposed in this paper. Scale Invariant Feature Transform (SIFT) is used to detect key points in the image. SIFT is both a feature detector and descriptor. The common region between different images is identified by comparing the feature descriptors of each image. Brute-Force matcher with KNN algorithm is used for feature matching. The outliers in the matching features are eliminated by Random Sample Consensus (RANSAC) algorithm. To create seamless image, alpha blending operation is applied. Experiments are conducted on UDISD (Unsupervised Deep Image Stitching Data set). The overall performance of the proposed stitching method is evaluated based on metrics such as PSNR, SSIM, RMSE, MSE and UIQI, and the proposed stitching algorithm yields good result with seamless stitched image.