Neural networks were successfully used for multicomponent kinetic determinations of species with rate constant ratios approaching unity without the aid of spectral discrimination. The ensuing method relies on two inputs describing the profile of the kinetic curve for each mixture, which is obtained by preprocessing kinetic data using nonlinear least-squares regression. A straightforward network architecture (2:4s:21) was used to resolve mixtures of 2- and 3-chlorophenol; the trained network estimated the concentrations of both components in the mixture with a relative standard error of prediction of approximately 5%, which is much lower than that obtained with Kalman filtering. The effect of some variables such as the rate constant and analyte concentration ratios on the proposed multicomponent determination is discussed.