Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capability of LDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extracts the features that help the classifier to classify the input face images. The classifier employed was SFAM. Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’s efficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated frequently. SFAM thus proves itself to be an efficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.