This paper presents a hybrid approach to enhance the performance of the data-based Pattern Classification Techniques (PCTs) used for Fault Detection and Diagnosis (FDD) of nonlinear dynamic noisy processes. The method combines kriging metamodels with PCT (e.g. Support Vector Machines). The metamodels are used in two different ways; first, they are used as Multivariate Dynamic Kriging(s) (MDKs) which estimate the process dynamic behavior/outputs, second, as classical static models which are used for smoothing noise and imputing missing values of the process actual outputs measurements. So during the process operations, the estimated and the smoothed actual outputs are compared, and residual/error signals are generated that is used by the classifier to detect and diagnose the process possible faults. The method is applied to a benchmark case study, showing a high enhancement in such PCTs due to the introduction of the process dynamics information to these PCTs via the MDKs, and by smoothing the noise and imputing the missing measurements using the static kriging.