The Metropolis Monte Carlo (MMC) Annealing is presented. In this approach to deconvolution two Monte Carlo Procedure (MCP) are run at the same time. In one the blurred data is used as a distribution function for selection of pixels. And the second MCP decides whether to place a grain in the true data (true input) or not. We show that this approach improves the annealing procedure drastically as compared to selection of pixels one at a time or from a flat distribution. The blurred data is obtained by convolving a 24 points input signal that has three peaks with a 21 points wide Gaussian impulse response function. The Mean Squared Error (MSE) is used to compare the two techniques. The MSE is calculated by comparing the reconstructed input signal with the true input signal. The MSE in reconstructed blurred data performed by MMC is also plotted vs. Monte Carlo move. Finally, the reconstructed input signal by MMC techniques is given at MSE of 39.