This article re-examines the implications of interior-node recognition when focussing on its efficient yet seamless integration into modern chess programs. By means of a thorough discussion of its fundamental principles, we reveal various problems related to the practical application of interior-node recognition. Then, the rest of the article presents an implementation framework for recognizers that solves all known problems and has already proven its practical viability in our high-speed chess program DARKTHOUGHT.
Among others we introduce the new concept of material signatures which allow for a quick and easy classification of chess positions into different categories of material distribution. By including material signatures in the internal representation of the chess board, they can incrementally be updated during the execution of moves. This makes the computation of material signatures extremely cheap in practice.