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The proliferation dynamics of a growing cell population can be represented by a discrete-time state model. An application of recursive least squares algorithms to the estimation of important cell cycle kinetic parameters is considered. Cell kinetic parameters are estimated recursively by the following steps: 1) decomposition of state and output spaces, 2) separation of identification of the unperturbed cell system from that of the perturbed cell system, and 3) application of a recursive least squares algorithm for the identification of each decomposed system. This method is feasible due to the availability of a new technology called flow microfluorometry (FMF) which is capable of providing large amounts of quantitative data within a short time period. Emphasis is placed on the construction of a computationally efficient and stable algorithm. The FMF deoxyribonucleic acid (DNA) data of a Chinese hamster ovary (CHO) cell population is used to demonstrate the potential value of the method developed.