Software effort estimation is an important step in software development. It predicts the amount of effort and development time required to build a software system. It is one of the most important tasks and an accurate estimate is vital to the successful completion of the project. Building software effort estimation requires developing sound computational models. This paper investigates the use of fuzzy-neural systems in estimating software effort. A comparison is made with a radial basis neural network. Results obtained based on the China dataset indicates that a hybrid model that combine fuzzy inferencing with neural networks ability to learn from examples provided more accurate results than using neural networks alone.