This paper proposes a neural-symbolic framework for analyzing a large volume of sequential hi-speed images of combustion flame for early detection of instability that is extremely critical for engine health monitoring and prognostics. The proposed hierarchical approach involves extracting low-dimensional semantic features from images using deep Convolutional Neural Networks (CNN) followed by capturing the temporal evolution of the extracted features using Symbolic Time Series Analysis (STSA). Furthermore, the semantic nature of the CNN features enables expert-guided data exploration that can lead to better understanding of the underlying physics. Extensive experimental data have been collected in a swirlstabilized dump combustor at various operating conditions for validation.