Sign language is a complex way of communication mostly used for deaf people where hands, limbs, head and facial expressions are used to communicate. Finger spelling is a system where each letter of the alphabet is represented by a unique and discrete movement of the hand. In this paper, we are interested in studying the properties of the spatial pyramid matching descriptor for finger spelling recognition. This method is a simple extension of an orderless bag-of-features image representation where local features are mapped to multi-resolution histograms and compute a weighted histogram intersection. The performance of the approach is evaluated on a dataset of real images of the American Sign Language (ASL) finger spelling. We conduct experiments considering three evaluation protocols. The first uses 10% of the data as training and the remaining as test, we achieve an accuracy rate of 92.50%. The second protocol considers 50% as training data, the accuracy rate was about 97.1%. Finally, in the third protocol, we perform a 5-fold cross-validation, where we achieve an accuracy rate of 97.9%. Our method achieves the best results in all three protocols when compared to state-of-the-art approaches. In all the experiments, we also evaluate the influence of the weights of the multi-resolution histograms. They do not have a significant influence in the experimental results.