Face Recognition has become one of the most widely researched areas in the Biometric Identification domain. Its popularity is due mostly to the fact that it is less intrusive than other biometric systems thus making it highly applicable to fields such as law enforcement and surveillance. Although there are a vast number of papers that are published yearly, there seems to be no comprehensive guide as to how to actually implement the most read about face recognition methods. In this thesis we try to answer this question by providing the steps that these face recognition algorithms follow to arrive at their output. We studied Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). In addition we also studied the Kernel version of PCA and LDA. We performed experiments using the five face recognition methods on the AT&T, YALE and NTHU PRIP face databases. The classification rates are reported in the experimental results section.