Tree based regression models like a M5 algorithm represent a promising development in machine learning research. A recent study suggests that a M5 model tree algorithm can be used for classification problems after some modification. This letter explores the usefulness of a M5 model tree for classification problems using multispectral (Landsat-7 Enhanced Thematic Mapper Plus (ETM+)) for a test area in eastern England. Classification accuracy achieved by using a M5 model tree is compared with a univariate decision tree with and without using boosting. Results show that the M5 model tree achieves a significantly higher level of classification accuracy than a decision tree and works equally well to a boosted decision tree. Further, a model tree based classification algorithm works well with small as well as noisy datasets.