The performance of Kriging interpolation for enhancement, smoothing, reconstruction and optimization of a test data set is investigated. Specifically, the ordinary two-dimensional Kriging and 2D line Kriging interpolation are investigated and compared with the well-known digital filters for data smoothing. We used an analytical 2D synthetic test data with several minima and maxima. Thus, we could perform detailed analyses in a well-controlled manner in order to assess the effectiveness of each procedure. We have demonstrated that Kriging method can be used effectively to enhance and smooth a noisy data set and reconstruct large missing regions (black zones) in lost data. It has also been shown that, with the appropriate selection of the correlation function (variogram model) and its correlation parameter, one can control the 'degree' of smoothness in a robust way. Finally, we illustrate that Kriging can be a viable ingredient in constructing effective global optimization algorithms in conjunction with simulated annealing.