Segmentation of large images can be one of the most time-consuming steps in the analysis of materials science datasets. Convolutional neural networks (NNs) have been shown to reduce segmentation time compared to manual techniques, but training a new NN is often required for each dataset. We show that simply combining NN training datasets does not necessarily lead to a NN capable of segmenting multiple types of images. In the present study, we first show that SegNet-based neural networks (NNs) can be trained to accurately segment Al-Zn x-ray computed tomography and Pb-Sn serial sectioning images. Applying the Al-Zn NN to the Pb-Sn test image led to misclassified smudges as dendrites, and misclassified speckles as background. Applying the Pb-Sn NN to the Al-Zn test image was unsuccessful, likely because the Al-Zn dendrites had a higher luminance than the Pb-Sn dendrites. The Mix NN (trained using the combined Al-Zn and Pb-Sn datasets) was better at segmenting the Pb-Sn test image than the Al-Zn test image. This is likely because the Pb-Sn training dataset contained ~4.5 times as many dendrite pixels as the Al-Zn training dataset, thus the Mix NN was over-tuned to identify Pb-Sn dendrites. Simply combining the training datasets was overall detrimental to NN performance, but assigning different classes to the Al-Zn and Pb-Sn dendrites may lead to enhanced performance in the future. These findings serve as guidelines in the quest to develop a universal NN for segmentation of large materials science datasets.