The dynamic performance of railway vehicle is normally expressed as stability, safety and ride comfort, and is affected by mass properties, suspension characteristics, contact mechanism between a wheel and a rail, etc. This paper describes the procedure of sensitivity analysis between some of the suspension characteristics of the Korean high speed train (KHST) as the design variables and the dynamic performance as the response variables; and it analyzes the results of sensitivity characteristics for the design variables, comparing two different approximated approach processes known as the response surface model formulated in a polynomial equation and neural network model formulated in a processing code. Analyzing the suspension characteristics for KHST, the approximated method creating meta-models consisted of 29 design variables and 46 performance indexes, which are applied in this paper. The models were coded by using the correlation information between the design variables and the performance indexes made by the 66 times iterative simulations according to the design of experimental method. The table consists of the orthogonal array L32 and the D-Optimal design table. The results show that the proposed sensitivity analysis procedure is very efficient and simply applicable for a complex mechanical system such as railway vehicle system. Also they show that the two models applied in this paper have similar tendency in the view of the sensitivity order of the design variables.