Face Recognition is the process of identification of a person by their facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification etc. Face recognition approaches for still images can be broadly categorized into holistic methods and feature based methods. Holistic methods use the entire raw face image as an input, whereas feature based methods extract local facial features and use their geometric and appearance properties. The present thesis primarily focuses on Principal Component Analysis (PCA), for the analysis and the implementation is done in software, MATLAB. This face recognition system recognizes the faces where the pictures are taken by web-cam or a digital camera are given as test database and these face images are then checked with training image dataset based on descriptive features]. Descriptive features are used to characterize images. It describes how to build a simple, yet a complete face recognition ] system using Principal Component Analysis, a Holistic approach. This method applies linear projection to the original image space to achieve dimensionality reduction. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features known as eigenfaces do not necessarily correspond to features such as ears, eyes and noses. It provides for the ability to learn and later recognize new faces in an unsupervised manner. This method is found to be fast, relatively simple, and works well in a constrained environment. © 2005 - 2015 JATIT & LLS. All rights reserved.