We present a novel method for fusing the results of multiple landmine detection algorithms that use different types of features, different classification methods, and different Sensors. The proposed fusion method, called Context-Dependent Multi-Sensor Fusion (CDMSF) is motivated by the fact that the relative performance of different detectors can vary significantly depending on the sensor, mine type, geographical site, soil and weather conditions, and burial depth. The training part of CDMSF has two components: context extraction and algorithm fusion. In context extraction, the features used by the different algorithms are combined and used to partition the feature space into groups of similar signatures, or contexts. The algorithm fusion component assigns an aggregation weight to each detector in each context based on its relative performance within the context. Results on Ground Penetrating Radar (GPR) and Wideband Electromagnetic induction (WEMI) data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our initial experiments have also indicated that the context-dependent fusion outperforms all individual detectors.