We present a comparison of neural network-based morphological segmenters trained on morphologically segmented datasets from seven European languages: Czech, English, French, German, Italian, Dutch, and Slovak. Our aim is to investigate how different model architectures and dataset sizes influence segmentation quality, and how performance varies across languages. To this end, we evaluate recurrent and convolutional neural network models and compare them to widely used unsupervised baseline methods. In selecting the datasets, we prioritized linguistic accuracy and segmentation completeness. We also explore the impact of cross-lingual transfer learning on model performance. Our results show that neural models trained on as few as 125 words outperform unsupervised methods. Moreover, for closely related languages, zero-shot cross-lingual transfer learning can also surpass unsupervised baselines. Overall, we observe consistent performance patterns across languages.