Technological systems are designed to carry out very specific functions. Because of that, their components should have measurements that can guarantee their operability within the range of precision. Furthermore, the current systems are inherent parts of design involving multi-disciplinary aspects. Their development and analysis expose the designer to a series of unknown parameters from several sources such as material properties, environmental and operational conditions. Therefore, the qualification and quantification of these inherent sources of design uncertainties become very important in several aspects in the context of design development and so, a system is reliable and robust if it allows a certain range of uncertainties before the first failure occurs. With this in mind, we propose here the development of a methodology that can identified the sources of uncertainties and parameters that largely influence the whole design. An initial study focuses on a simple oscillatory system that consists of a mass, a spring and a damper. The full methodology developed in this paper consists in choosing the element or mechanical system and in choosing the experiment design for identifying the critical parameters (factorial designs or fractional designs). This proposal to the development of polynomial models (linear or quadratic) that should fit the experimental results from the factorial design. Once the critical parameters are obtained there is a search for optimum regions maximum, minimum or singular point. The steps used in the search interval occur along maximum or minimum lines that describe a region of interest or experimentation. The Response Surface Methodology (RSM) was chosen because this method searches the optimum region for each parameter and their fitting. A sensitivity analysis also takes place using canonical analysis and optimization methods for parameter fittings such the Steepest Ascent and/or Simplex Method. The last step is the confidence limits analysis, which searches for these parameters through the reliability concepts extended to convex models. Such models use closed regions for estimate and quantify the allowable ranges of variability to each parameter with respect to the critical parameters in the robust design concepts. Finally, the methodology is used in a case study of a mass-spring-damping system, and in the future, it is going to be applied systems such foundation structures and rotor-bearings systems. Copyright © 2002 Society of Automotive Engineers, Inc. Asimow, M., (1968), p. 171. , Introdução ao projeto de engenharia. Ed. Jou MestreAchcar, J.A., , p. 251. , Planejamento de experimentos em engenharia e indústria (Apostila) ICMSC-USP, São Carlos, São PauloBen-Haim, Y., (1996) Robust Reliability in the Mechanical Sciences, p. 232. , Springer-Verlag, BerlinBox, G.E.P., Hunter, W.G., Hunter, J.S., (1978) Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, p. 652. , John Wiley & SonsBox, E.P., Draper, N.R., (1987) Empirical Model-building and Response Surfaces, , John Wiley & Sons: New YorkCavalca, K.L., Dedini, F.G., Supporting Structure Effects on Rotating Machinery Vibrations (1992) ImechE Proceedings of International Conference on Vibrations in Rotating Machinery, pp. 543-548Chen, W., A procedure for robust design: Minimizing variations caused by noise factors and control factors (1996) Transactions of the ASME, 118, pp. 478-485Chen, W., Exploration of the effectiveness of physical programming in robust design (2000) Journal of Mechanical Design, 122, pp. 155-163Dunsmore, W., Developing methodologies for robust mechanical engineering design (1997) Proceedings Instn. Mech. Engineers. ImechE, pp. 179-188Ferreira, P.A.V., (2000), Apostila de otimização não-linear. UNICAMP, FEEC, Departamento de TelemáticaKunjur, A., Krishnamurty, S., A robust multi-criteria optimization approach (1997) Mechanical Machine Theory, 32 (7), pp. 797-810Martin, P., A review of mechanical reliability (1998) Proceedings Instn. Mech. Engineers. ImechE, pp. 281-287Myers, R.H., Montgomery, D.C., (1995) Response Surface Methodology: Process and Product Optimization Using Designed Experiments, p. 702. , John Wiley & Sons, IncMontgomery, D.C., (1991) Design and Analysis of Experiments, , John Wiley & SonsPhadke, M.S., (1989) Quality Engineering Using Robust Design, p. 334. , PTR Prentice HallRustaji, J.S., (1994) Optimization Techniques in Statistics, p. 358. , Academic PressParkinson, D.B., Robust design employing a genetic algorithm (2000) Quality and Reliability Engineering International, pp. 201-208