The use of artificial neural networks to the channel assignment problem for cellular code- division multiple access (CDMA) telecommunications systems is considered. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth limited. Any reduction in interference in CDMA translates linearly into increased capacity. FDMA and TDMA use a frequency reuse pattern as a method to increase capacity, while CDMA reuses the same frequency for all cells and gains a reuse efficiency by means of orthogonal codes. The latter method can improve system capacity by factors of four to six over digital TDMA or FDMA. Cellular carriers are planning to provide multiple communication services using CDMA in the next generation cellular system infrastructure. The approach of this study is the use of neural network methods for automatic and local network control, based on traffic behavior in specific cell cites and demand history. The goal is to address certain problems associated with the management of mobile and personal communication services in a cellular radio communications environment. In planning a cellular radio network, the operator assigns channels to the radio cells so that the probability of the processed carrier-to-interference ratio, CII, exceeding a predefined value is sufficiently low. The RF propagation, determined from the topography and infrastructure in the operating area, is used in conjunction with the densities of expected communications traffic to formulate interference constraints. These constraints state which radio cells may use the same code (channel) or adjacent channels at a time. The traffic loading and the number of service grades can also be used to calculate the number of required channels (codes) for each cell. The general assignment problem is the task of assigning the required number of channels to each cell such that these constraints are satisfied. This study applies and extends the Hopfield-Tank neural network models to the channel assignment problem for both uniform and non-homogeneous cellular CDMA network topologies. These models are shown to be applicable to future networks that provide multiple types of service, dynamic demand, and mobile base stations. The derived algorithms minimize energy functions representing interference constraints and traffic demand based on local data at the cell sites. The primary objectives of the approach are to increase the forward and reverse link capacities and to distribute selected management tasks at the Mobile Telecommunications Switching Office to the cell sites. The structure of the resulting neural network algorithms have the advantage of inherent parallelism and the potential for extension to a wide range of interference criteria. Two cases are considered. In the first case, traffic demands are uniform over the radio cells, while the radio cells are assumed to be a fixed hexagonal pattern. The second case corresponds to an urban cellular radio environment, where the location of the radio cells are not homogeneous and the spatial distribution of traffic demand are non-uniform.