This paper proposes a new regularization method based on the MDL (Minimum Description Length) principle. An adequate precision weight vector is trained by approximately truncating the maximum likelihood weight vector. The main advantage of the proposed regularizer over existing ones is that it automatically determines a regularization factor without assuming any specific prior distribution with respect to the weight values. Our experiments using regression problems showed that the MDL regularizer significantly improves the generalization error of a second-order learning algorithm and shows a comparable generalization performance to the best tuned weight-decay regularizer.