Physical models are used to model reflections from target primitives commonly encountered in mobile robot applications. These targets are differentiated by employing a multi-transducer pulse/echo system which relies on both amplitude and time-of-flight (TOF) data in the feature fusion process, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused for multiple logical sonars at different geographical sites. This evidential approach helps to overcome the vulnerability of echo amplitude to noise and enables the modeling of non-parametric uncertainty. Feature data from multiple logical sensors are fused with Dempster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Using three sensing nodes, improvement in differentiation is between 20-40% without false decision, at the cost of additional computation. Simulation results are verified by experiments with a real sonar system. This evidential approach helps to overcome the vulnerability of the echo amplitude to noise and enables the modeling of non-parametric uncertainty in real time.