A challenging real-time imaging problem is classifying video traffic signs in background clutter under rotation, scale, and translation invariant conditions. Normalized Gabor Wavelet Transform features from multi-resolution filters were originally biologically-based; however, optimized features proved more effective. Two whole image template matching techniques were unsuccessful. A statistical pattern recognition system recognized approximately 30% of the images for the original features and 50% for the optimized features; however, a multi-layer perceptron (mlp) detected over 70% of the images with the optimized features. The research demonstrated the possibility of a future automotive navigation aid which robustly collects sign images and classifies these images in real-time with a single Fast Fourier Transform (FFT), a bank of filters and a trained neural net.