Real world data mining applications such as Mine Countermeasure Missions (MCM) involve learning from imbalanced data sets, which contain very few instances of the minority classes and many instances of the majority class. For instance, the number of naturally occurring clutter objects (such as rocks) that are detected typically far outweighs the relatively rare event of detecting a mine. Such data sets always make a default lassifier of little use because of skewed vector spaces or lacking information. In this research we propose support vector machine with adaptive asymmetric misclassification costs to solve the skewed vector spaces problem in mine countermeasure missions. Experimental result shows that the given algorithm could be used for imbalanced sonar image data sets and make an improvement in prediction performance.