Recognition of spatial relations between pairs of subexpressions is a key problem of recognition of handwritten mathematical expressions. Most methods for spatial relation classification are based on handcrafted rules and geometric indices extracted from the subexpression bounding boxes. In this work, we propose new spatial relation features that combine subexpression bounding box and intra-subexpression information, along with prior knowledge about the general position and size of symbols. Instead of handcrafting features, we train artificial neural networks to learn the useful features from two kinds of histograms. The first type captures the relative positions and sizes of the subexpression bounding boxes. The second captures the relative positions and shape of a pair of symbols, called dominant symbols, extracted from the main baselines of the evaluated subexpressions. We evaluate and compare our features with two state-of-the-art features on a benchmark dataset. Experimental results show that our features obtain better accuracy than these two features.