In any game, play-style is a concept that describes the technique and strategy employed by a player to achieve a goal. Being able to identify the play-style of a player is desirable as it can enlighten players on which approaches work better or worse in different scenarios, as well as inform developers of the value of design decisions. In this paper, we propose a novel approach to play-style identification based on an unsupervised LSTM-autoencoder clustering approach for multi-dimensional trajectory-based data of variable length. We evaluate our approach on two domains and show that not only is our model capable of identifying these play-styles from entire trajectories but it is also capable of this during gameplay from partial trajectories. Additionally, it is demonstrated through state frequency analysis that the properties of each of the play-styles can be identified and compared. Through these processes, we can extract useful information which describes the different behaviours or play-styles present within a domain useful to both players and developers.