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Content-based image retrieval plays an increasing role in the clinical process for supporting diagnosis. This paper proposes a neighbourhood search method to select the near-optimal feature subsets for the retrieval of mammograms from the Mammographic Image Analysis Society (MIAS) database. The features based on grey level cooccurrence matrix, Daubechies-4 wavelet, Gabor, Cohen-Daubechies-Feauveau 9/7 wavelet and Zernike moments are extracted from mammograms available in the MIAS database to form the combined or fused feature set for testing various feature selection methods. The performance of feature selection methods is evaluated using precision, storage requirement and retrieval time measures. Using the proposed method, a significant improvement is achieved in mean precision rate and feature dimension. The results show that the proposed method outperforms the state-of-the-art feature selection methods.