In this paper, we propose a novel method, called the nearest feature midpoint (NFM), for pattern classification. Any two feature points of the same class are generalized by the feature midpoint (FM) between them. The representational capacity of available prototypes is thus expanded. The classification is based on the nearest distance from the query feature point to each FM. A theoretical proof is provided in this paper to show that for the n-dimensional Gaussian distribution, the classification based on the NFM distance metric achieves the least error probability as compared to those based on any other points on the feature lines. Furthermore, a theoretical investigation indicates that under some assumption the NFL is approximately equivalent to the NFM when the dimension of the feature space is high. The empirical evaluation on a simulated data set concurs with all the theoretical investigations.