A class-modular generalized learning vector quantization (GLVQ) ensemble method with outlier learning for handwritten digit recognition is proposed. A GLVQ classifier is one of discriminative methods. Though discriminative classifiers have remarkable ability to solve character recognition problems, they are poor at outlier resistance. To overcome this problem, a GLVQ classifier trained with both digit images and outlier images is introduced. Moreover, the original 10-classification problem is separated into ten 2-classification problems using ten GLVQ classifiers, each of which recognizes its corresponding digit class. Experimental results of handwritten digit recognition and outlier rejection reveal that our method is far more superior at outlier resistance than a conventional GLVQ classifier, while maintaining its digit recognition performance.