Star-CNN is a particular architecture of Cellular Neural Networks that has been recently proposed. This dynamic nonlinear system is defined by connecting N identical dynamical sysstem called local cell with a Central System in the shape of a star. Each of the local cells communicates with others through the Central System. Because of the hardware requirements of such a system, its implementation comes out extremely expansive from the silicon area occupation point of view. This paper presents a hardwaare implementation of this new CNN architecture based on a time division approach that allows to significantly reduce the silicon area occupation by minimizing the number of the analogue multipliers. © 2008 IEEE.