Multivariate analysis and filtering techniques are widely applied to simultaneous and/or selective determination of multicomponent systems. Many methods among them are based on the principle of linear addition, while this principle does not always hold due to various physical and chemical factors. Using quite a different way, neural network (NN) based on a given learning rule, such as back propagation (BP) model, needs neither knowing nor using any form of input/output relationship. Particularly, NN can resolve various problems such as those with casual relation, those with fuzzy backgrounds, and those with uncertain inferential processes. NN was used by us to investigate quantitative struc- Multivariate analysis and filtering techniques are widely applied to simultaneous and / or selective determination of multicomponent systems. Many methods among them are based on the principle of linear addition, while this principle does not always hold due to various physical and chemical factors. Particularly, NN can resolve various problems such as those with casual relation , those with fuzzy backgrounds, and those with uncertain inferential processes. NN was used by us to investigate quantitative struc-