Although the performance of person re-identification (Re-ID) has been much improved by using sophisticated training methods and large-scale labelled datasets, many existing methods make the impractical assumption that information of a target domain can be utilized during training. In practice, a Re-ID system often starts running as soon as it is deployed, hence training with data from a target domain is unrealistic. To make Re-ID systems more practical, methods have been proposed that achieve high performance without information of a target domain. However, they need cumbersome tuning for training and unusual operations for testing. In this paper, we propose augmented hard example mining, which can be easily integrated to a common Re-ID training process and can utilize sophisticated models without any network modification. The method discovers hard examples on the basis of classification probabilities, and to make the examples harder, various types of augmentation are applied to the examples. Among those examples, excessively augmented ones are eliminated by a classification based selection process. Extensive analysis shows that our method successfully selects effective examples and achieves state-of-the-art performance on publicly available benchmark datasets.