Hashing algorithms which encode signal content into compact binary codes to preserve similarity, have been extensively studied for applications such as large-scale visual search. However, most existing hashing algorithms work with a single feature type, while combining multiple features is helpful in many vision tasks. In this paper, we propose two multi-feature hashing algorithms based on signal-to-noise ratio (SNR) maximization, where a globally optimal solution is obtained by solving a generalized eigenvalue problem. The first one jointly considers all feature correlations and learns uncorrelated hash functions that maximize SNR, and the second algorithm separately learns hash functions on each individual feature and selects the final hash functions based on the SNR associated with each hash function. The proposed algorithms perform favorably compared to other state-of-the-art multi-feature hashing algorithms on several benchmark datasets.