Image segmentation is an important preprocesing procedure for image analysis. The result of image segmentation significantly affects the accuracy of subsequent image analysis. The Bayesian and neural network classification methods are widely used for automatic and supervised image segmentation. The objectives of this research were to establish image segmentation methods for various background and lighting conditions based on Bayesian and neural network methods, and to compare the efficacy and feasibility of applying these two methods under different conditions. Software components employing object oriented programming language for color image segmentation was initially built and tested with 2-dimensional data sets. These data sets were represented with 2-dimensional plots corresponding to linearly separable, non-convex, and mutually included classification patterns. The data sets were classified with both the Bayesian and neural network methods, and the results were compared and discussed. Finally, color images of selected vegetable seedling sunder different background and lighting conditions were tested. The results was compared along with the spatial distribution of pixels in RGB color coordinates. The experimental results indicated that under good lighting and uncomplicated background conditions, the use of Bayesian and neural network methods were not significantly different. The average errors of image segmentation were all below 1%.However, for images with complex background, image segmentation using neural network showed better result than the Bayesian method. In terms of the sampling requirement during the learning phase, the neural network method showed better robustness.