High pressure die casting (HPDC) has been developed since the late nineteenth century for a breadth of manufacturing applications. The process forms molten metal into molds at high temperatures given a complex array of parameters and variables that are challenging to observe. We used a set of thermal cameras to capture imagery of the die used as a mold during its cooling process between part productions. This data was used to train a convolution neural network to assess the quality of the part just produced based on the thermal characteristics of the surface of the die. The system achieved 90% accuracy when distinguishing between parts that met quality standards and parts that did not.