Abstract: Image analysis is an essential aspect of microarray experiment: measures over the scanned image can substantially affect successive steps such as clustering and identification of differentially expressed genes. Scanned microarray image processing has three main tasks: (i) gridding, which is the process of assigning the coordinates to the spots, (ii) segmentation, it allows the separation between foreground and background pixels, and (iii) intensity extraction. Most of available gridding approaches require human intervention, for example to specify some points in the spot grid or even to register individual spots. Automating this part of the process will allow high throughput analysis. The paper reports a novel approach for the problem of automatic gridding in Microarray images. The method uses a two step process. First a regular rectangular grid is superimposed on the image by interpolating a set of guide spots, this is done by solving a non-linear optimization process with an evolutionary approach. Second, the interpolating grid is adapted, with Markov Chain Monte Carlo method, to local deformations. This is done by modeling the solution as a Markov Random Field with a Gibbs prior possibly containing first order cliques (1-clique). The algorithm is completely automatic and no human intervention is required, it efficiently accounts grid rotations and irregularities.