We present the assessment of two classification procedures using a Monte Carlo experience and Landsat data. Classification performance is hard to assess with generality due to the huge number of variables involved. In this case we consider the problem of classifying multispectral optical imagery with pointwise Gaussian Maximum Likelihood and contextual ICM (Iterated Conditional Modes), with and without errors in the training stage. Using simulation the ground truth is known and, therefore, precise comparisons are possible. The contextual approach proved being superior than the pointwise one, at the expense of requiring more computational resources, with both real and simulated data. Quantitative and qualitative results are discussed.