We present a comparison of two methods for color based road segmentation. The first was implemented using a neural network, while the second approach is based on support vector machines. A large number of training images were used with varying road conditions including roads with snow, dirt or gravel surfaces, and asphalt. We experimented with grouping the training images by road condition and generating a separate model for each group. The system would automatically select the appropriate one for each novel image. Those results were compared with creating a single model with all images. In another set of experiments, we added the image coordinates of each point as an additional feature in the models. Finally, we compared the results and the efficiency of neural networks and support vector machines of segmentation with each combination of feature sets and image groups.