This study describes a system for interactive annotation of thoracic CT scans. Lung volumes in these scans are segmented and subdivided into roughly spherical volumes of interest (VOIs) with homogeneous texture using a clustering procedure. For each 3D VOI, 72 features are calculated. The observer inspects the scan to determine which textures are present and annotates, with mouse clicks, several VOIs of each texture. Based on these annotations, a k-nearest-neighbor classifier is trained, which classifies all remaining VOIs in the scan. The algorithm then presents a slice with suggested annotations to the user, in which the user can correct mistakes. The classifier is retrained, taking into account these new annotations, and the user is presented another slice for correction. This process continues until at least 50% of all lung voxels in the scan have been classified. The remaining VOIs are classified automatically. In this way, the entire lung volume is annotated. The system has been applied to scans of patients with usual and non-specific interstitial pneumonia. The results of interactive annotation are compared to a setup in which the user annotates all predefined VOIs manually. The interactive system is 3.7 times as fast as complete manual annotation of VOIs and differences between the methods are similar to interobserver variability. This is a first step towards precise volumetric quantitation of texture patterns in thoracic CT in clinical research and in clinical practice.