The prudent selection of the sampling points ensures that representative points to typify a feature surface are obtained. The rationale is that the larger the number of sample points, the better the estimate of the surface. Large samples however lead to large measurement times and consequently time-induced errors. It is believed that a priori knowledge of process-induced errors can help in minimizing the total number of sampling points. Modeling the initial points for search, or approximate locations of errors is the key to minimizing the sampling effort. Suitable search methodology can then be used to determine the actual location of errors. If the regression surface describing the actually measured points can be identified, an adaptive search can be conducted. To do this we are using a kind of function learning machines that has been extensively developed the last decade, the Support Vector Regression (SVR). This paper describes in general terms our methodology.