A novel hierarchical unbiased group-wise registration is developed to robustly transform each individual image towards a common space for atlas based analysis. This hierarchical group-wise registration approach consists of two main components, (1) data clustering to group similar images together and (2) unbiased group-wise registration to generate a mean image for each cluster. The mean images generated in the lower hierarchical level are regarded as the input images for the higher hierarchy. In the higher hierarchical level, these input images will be further clustered and then registered by using the same two components mentioned. This hierarchical bottom-up clustering and within-cluster group-wise registration is repeated until a final mean image for the whole population is formed. This final mean image represents the common space for all the subjects to be warped to in order for the atlas based analysis. Each individual image at the bottom of the constructed hierarchy is transformed towards the root node through concatenating all the intermediate displacement fields. In order to evaluate the performance of the proposed hierarchical registration in atlas based statistical analysis, comparisons were made with the conventional group-wise registration in detecting simulated brain atrophy as well as fractional anisotropy differences between neonates and 1-year-olds. In both cases, the proposed approach demonstrated improved sensitivity (higher t-scores) than the conventional unbiased registration approach.