An Intel RealSense camera is used for translating static manual American Sign Language gestures into text. The system uses palm orientation and finger joint data as inputs for either a support vector machine or a neural network whose architecture has been optimized by a genetic algorithm. A data set consisting of 100 samples of 26 gestures (the letters of the alphabet) is extracted from 10 participants. When comparing the different learners in combination with different standard preprocessing techniques, the highest accuracy of 95% is achieved by a support vector machine with a scaling method, as well as principal component analysis, used for preprocessing. The highest performing neural network system reaches 92.1% but produces predictions much faster. We also present a simple software solution that uses the trained classifiers to enable user-friendly sign language translation.