We develop and test machine learning-based tools for the classification of personal relationships in biographical texts, and the induction of social networks from these classifications. A case study is presented based on several hundreds of biographies of notable persons in the Dutch social movement. Our classifiers mark relations between two persons (one being the topic of a biography, the other being mentioned in this biography) as positive, neutral, or unknown, and do so at an above-baseline level. A training set centering on a historically important person is contrasted against a multi-person training set; the latter is found to produce the most robust generalization performance. Frequency-ranked predictions of positive and negative relationships predicted by the best-performing classifier, presented in the form of person-centered social networks, are scored by a domain expert; the mean average precision results indicate that our system is better in classifying and ranking positive relations (around 70% MAP) than negative relations (around 40% MAP).