The concept of distance, its role in pattern recognition, and some advantages of the new model for pattern learning proposed recently by the author are discussed. The universality, flexibility, and the ability to connect intrinsically the low-level process that selects the primitives for the pattern representation with the higher level recognition process make the model clearly superior to other models proposed so far. The fundamentally new analytical feature of the model, which allows the learning machine to reconfigure itself efficiently, is the introduction of continuity in the classical discrete computational model.