The combination of local features, complementary feature types, and relative position information has been successfully applied to many object-class recognition tasks. Stacking is a common classification approach that combines the results from multiple classifiers, having the added benefit of allowing each classifier to handle a different feature space. However, the standard stacking method by its own nature discards any spatial information contained in the features, because only the combination of raw classification scores are input to the final classifier. The object-class recognition method proposed in this paper combines different feature types in a new stacking framework that efficiently quantizes input data and boosts classification accuracy, while allowing the use of spatial information. This classification method is applied to the task of automated insect-species identification for biomonitoring purposes. The test data set for this work contains 4722 images with 29 insect species, belonging to the three most common orders used to measure stream water quality, several of which are closely related and very difficult to distinguish. The specimens are in different 3D positions, different orientations, and different developmental and degradation stages with wide intra-class variation. On this very challenging data set, our new algorithm outperforms other classifiers, showing the benefits of using spatial information in the stacking framework with multiple dissimilar feature types.