This letter presents a new technique for clustering hyperspectral images that exploits neighborhood-constrained spatial information. The main feature of the proposed method is the introduction of a neighborhood homogeneity index (NHI) and the use of this index to measure the spatial homogeneity in a local area. A new similarity measurement integrates NHI and spectral information using an adaptive distance norm for clustering. The performance of the proposed neighborhood-constrained-clustering algorithm was assessed through a synthetic image and a real hyperspectral image and compared with those obtained by advanced spectral-spatial clustering algorithms. Experimental results show that the proposed scheme gives better performances.