Principal component analysis (PCA) has been a major field of study in image compression, coding technique, or pattern recognition, particularly for classification and feature subset selection. Based on its success in these domains, character recognition methods using PCA have attracted considerable attention in recent years. In this paper, we propose a novel scheme for gray-scale handwritten character recognition based on principal of training set are projected onto the subspaces defined by their most important eigenvectors. Here, the significant eigenvectors of each class are chosen as those with the largest associated eigenvalues. These eigenvectors can be thought of as a set of feature vectors, that is, principal features. In this paper, we consider the minimum error subspace classifier for classification. It is a discriminant function derived from the PCA. We discriminate an unknown test character during the recognition phase by projection and classification. The recognition is performed by projecting a test image onto the subspace defined by the dominant eigenvectors of each class and then choosing the class corresponding to the subspace with the minimum error as the class of the test character. In order to verify the performance of the proposed scheme for gray-scale handwritten character recognition, experiments with the IPTP CDROM1 database have been carried out. Of the 12,000 samples available on this CD, 9,000 and 3,000 have been sued for training and testing, respectively. In this paper, we investigated the influence of the number eigencharacters used to define the subspace as well as the number of training characters for each character. Experimental results reveal that the proposed scheme based on principal features has advantages over other character recognition approaches in its speed and simplicity, learning capacity, and insensitivity to variations in the handwritten character images.