The concept of Negative Hesitation Fuzzy Sets (NHFSs) has been proposed recently. NHFSs show superiority in pattern recognition. In this paper, we propose different distance measures for NHFSs. The distance measures reflect the relationship between the various patterns constructed by NHFSs. The distance measures of the NHFSs can become the basis for creating the similarity measure. Meanwhile, the distance measures can be applied to feature extraction and selection in Machine Learning and Neural Networks when NHFSs represent the data in future work. Illustrative experiments are used to evaluate these distance measures. Failure Examples of different distance measures in pattern recognition problems will be demonstrated and explained. The comparison between different distance measures is also concluded. Distance measures are also applied to the Motor Imaginary Electroencephalogram (EEG) classification task, and the precision has been calculated.