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Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic programming (GP) is a powerful and flexible machine learning technique that has been successfully applied to many binary classification tasks. However, the traditional form of GP performs poorly on multiclass classification problems. Linear GP (LGP) is an alternative form of GP where programs are represented as sequences of instructions like Java and C++. This paper discusses results which demonstrate the superiority of LGP as a technique for multiclass classification. It also discusses a new extension to LGP which results in a further improvement in the performance on multiclass classification problems.