We employ machine learning methods to identify skill among active bond mutual fund managers. Using a comprehensive dataset of 3,021 unique U.S. bond funds from May 1995 to November 2024, we demonstrate that fund-level and family-level characteristics, particularly past performance metrics, reliably predict future bond fund performance. A prediction-weighted portfolio strategy that goes long the best-10% of funds and short the worst-10% of funds generates monthly abnormal returns of 33 basis points with an information ratio of 27.6%. The outperformance persists for up to 36 months. […]