This case-study suggests an automatic way for post-flight analysts to undertake classification of flight profiles into useful versus non-useful classes. Instead of using the traditional algorithms for time-series classification, this work makes use of a relatively new approach: Before classifying, first, transform a time-series into an image. This allows for the application of a well-developed set of algorithms from the area of computer vision. In this project, we perform a comparison of a number of these transformation techniques in terms of their associated image classification performance. We apply each transformation technique to the time-series dataset, in turn, train a Convolutional Neural Network to do classification, and record the performance. Then we select the most performant transformation technique (a simple line plot that got a 100% F1-score) and use it in the rest of the analysis pipeline.  The pipeline consists of three models. The first model classifies flight profiles into developed (useful) and non-developed (non-useful) profiles. The second model performs multi-label classification on the developed profiles from the first model. The labels reflect whether a profile has canonical climb/cruise/descent segments. The last model classifies flight profiles with canonical cruise segments into classes that have extended cruises (useful) and shorter cruises (non-useful).  Next, we prepare a significant unlabeled test dataset, consisting of data points that have never been seen by any of the models. We construct an end-to-end analytic inference process to simulate a production system, apply it to the test dataset, and obtain impressive results. Finally, we make recommendations to analysts.