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This Special Issue, comprised of four articles, is dedicated to recent developments in the application of machine learning algorithms to traffic sign recognition. The recognition of traffic signs is a challenging real-world problem, which is relevant for intelligent transportation systems participating in traffic environments. It poses a multicategory classification problem with unbalanced class frequencies. Traffic signs show a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. However, some subsets of classes are very similar to each other (e.g., speed limit signs). In addition to these interclass differences and similarities, the classifier has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc.