This work aims at automatically recognizing sequences of complex karate movements and giving a measure of the quality of the movements performed. Since this is a problem which intrinsically needs a 3D model, in this work we propose a solution taking as input sequences of skeletal motions that can derive from both motion capture hardware or consumer-level, off the shelf, depth sensing systems. The proposed system is constituted by four different modules: skeleton representation, pose classification, temporal alignment, and scoring. The proposed system is tested on a set of different punch, kick and defense karate moves executed starting from the simplest case, i.e. fixed static stances (heiko dachi) up to sequences in which the starting stances is different from the ending one. The dataset has been recorded using a single Microsoft Kinect. The dataset includes the recordings of both male and female athletes with different skill levels, ranging from novices to masters.