Abstract: The need for a secured personal identification system has led researchers into the field of biometrics. Currently a lot of research work is carried out in this area. One of the most promising traits is the palmprint which is widely used because of the ease with which it can be acquired, accuracy and stability. The palmprint contains principal lines, ridges and wrinkles which can be used to uniquely identify a person. It is user friendly, contains larger surface area and more features can be extracted using low resolution images. Also it is highly discriminative and even identical twins have different palmprint. In this proposed study, features are extracted from palmprint based on local gabor XOR pattern and principal component analysis. Each of these features is extracted and matched using Euclidean distance measure. Finally the scores generated by the individual matchers are combined using score level fusion. This fusion technique improves the performance of the biometric system and is found to provide low error rates and high recognition accuracy.