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Success of any image retrieval system depends heavily on the feature extraction capability of its feature descriptor. In this paper, we present a biomedical image retrieval system which uses Zernike moments (ZMs) for extracting features from CT and MRI medical images. ZMs belong to the class of orthogonal rotation invariant moments (ORIMs) and possess very useful characteristics such as superior information representation capability with minimum redundancy, insensitivity to image noise etc. Existence of these properties as well as the ability of lower order ZMs to discriminate between different image shapes and textures motivated us to use ZMs for biomedical retrieval application. To prove the effectiveness of our system, two experiments have been carried out on two different medical databases i.e. Emphysema-CT database for CT image retrieval and OASIS-MRI database for MR image retrieval. Investigation of results demonstrate the superior performance of the proposed system as compared to local binary pattern (LBP), local diagonal extrema pattern (LDEP) in terms of various evaluation measures like ARR, ARP, F_score and mAP.