Artificial neural networks is a popular field of research in artificial intelli- gence. The increasing size and complexity of huge models entail certain problems. The lack of transparency of the inner workings of a neural net- work makes it difficult to choose efficient architectures for different tasks. It proves to be challenging to solve these problems, and with a lack of in- sightful representations of neural networks, this state of affairs becomes entrenched. With these difficulties in mind a novel 3D visualization tech- nique is introduced. Attributes for trained neural networks are estimated by utilizing established methods from the area of neural network optimiza- tion. Batch normalization is used with fine-tuning and feature extraction to estimate the importance of different parts of the neural network. A combi- nation of the importance values with various methods like edge bundling, ray tracing, 3D impostor and a special transparency technique results in a 3D model representing a neural network. The validity of the extracted im- portance estimations is demonstrated and the potential of the developed visualization is explored.