Neural Networks and related Deep Learning methods are currently at the leading edge of technologies used for classifying objects. However, they generally demand large amounts of time and data for model training; and their learned models can sometimes be difficult to interpret. In this paper, we advance FastMapSVM—an interpretableMachine Learning framework for clas sifying complex objects—as an advantageous alternative to Neural Networks for general classification tasks. FastMapSVM combines the complementary strengths of FastMap and Support-Vector Machines. FastMap is an efficient linear-time algorithmthatmaps complex objects to points in a Euclidean space, while preserving pairwise non-Euclidean distances between them. We demonstrate the efficiency and effectiveness of FastMapSVM in the context of classifying seismograms. We show that its performance, in terms of precision, ecall, and accuracy, is comparable to that of other state-of-the-art methods. However, compared to other methods, FastMapSVMuses significantly smaller amounts of time and data for model training. It also provides a perspicuous visualization of the objects and the classification boundaries between them. We expect FastMapSVM to be viable for classification tasks in many other real-world domains.