The past few years have witnessed a growing recognition of soft computing technologies that underlie the conception, design and utilization of intelligent systems. According to Zadeh [1], soft computing consists of artificial neural networks, fuzzy inference system, approximate reasoning and derivative free optimization techniques. In this paper, we report a performance analysis among Multivariate Adaptive Regression Splines (MARS), neural networks and neuro-fuzzy systems. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. For performance evaluation purposes, we consider the famous Box and Jenkins gas furnace time series benchmark. Simulation results show that MARS is a promising regression technique compared to other soft computing techniques.