We investigate the problem of stroke-level sketch segmentation, which is to train machines to assign strokes with semantic part labels given a input sketch. Solving the problem of sketch segmentation opens the door for fine-grained sketch interpretation, which can benefit many novel sketch-based applications, including sketch recognition and sketch-based image retrieval. In this paper, we treat the problem of stroke-level sketch segmentation as a seqence-to-sequence generation problem, and a reccurent nueral networks (RNN)-based model SketchSegNet is presented to translate sequence of strokes into thier semantic part labels. In addition, for the first time a large-scale stroke-level sketch segmentation dataset is proposed, which is composed of 57K annotated free-hand human sketch selected from QuickDraw. Experimental results of stroke-level sketch segmentation on this novel dataset shows that our approach offers an average accuracy over 90% for stroke labeling.