The increasing numbers and complexity of spacecraft is driving a growing need for automated fault detection, isolation, and recovery. Anomalies and failures are common occurrences during space flight operations, yet most spacecraft currently possess limited ability to detect them, diagnose their underlying cause, and enact an appropriate response. This leaves ground operators to interpret extensive telemetry and resolve faults manually, something that is impractical for large constellations of satellites and difficult to do in a timely fashion for missions in deep space. A traditional hurdle for achieving autonomy has been that effective fault detection, isolation, and recovery requires appreciating the wider context of telemetry information. Advances in machine learning are finally allowing computers to succeed at such tasks. This dissertation presents an architecture based on machine learning for detecting, diagnosing, and responding to faults in a spacecraft attitude determination and control system. Unlike previous approaches, the availability of faulty examples is not assumed. In the first level of the system, one-class support vector machines are trained from nominal data to flag anomalies in telemetry. Meanwhile, a spacecraft simulator is used to model the activation of anomaly flags under different fault conditions and train a long short-term memory neural network to convert time-dependent anomaly information into a diagnosis. Decision theory is then used to convert diagnoses into a recovery action. The overall technique is successfully validated on data from the LightSail 2 mission. <br>