Traffic sign classification is a prime issue for autonomous platform industries such as autonomous cars. Towards the goal of recognition, most recent classification methods deploy Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs). In this work, we provide a novel dataset and a hybrid ANN that achieves accurate results that are very close to the state-of-the-art ones. When training and testing on German Traffic Sign Recognition Benchmarks (GTSRB) a top-5 classification accuracy of 80% was achieved for 43 classes. On the other hand, a top-2 classification accuracy of 95% was reached on our novel dataset for 10 classes. This accomplishment can be linked to the fact that the proposed hybrid ANN combines 9 different models trained on color intensity, HOG (Histograms of Oriented Gradients) and LBP (Local Binary Pattern) features.