While PyBrain offers a user-friendly framework for neural network experimentation, there are several common pitfalls and challenges that users may encounter:

* Limited Documentation : PyBrain's documentation is not as extensive as some other deep learning frameworks. Users may find it challenging to find comprehensive examples or detailed explanations for specific functionalities.

* Performance : PyBrain may not offer the same level of performance as some other deep learning libraries optimized for GPU computation, such as TensorFlow or PyTorch. Training large neural networks on large datasets using PyBrain may be slower compared to these frameworks.

* Lack of Advanced Features : PyBrain may lack some advanced features available in other deep learning libraries, such as built-in support for GPU acceleration, advanced regularization techniques, or pre-trained models.

* Limited Development and Support : PyBrain development has slowed down in recent years, and there may be fewer updates or bug fixes compared to active projects. Users may encounter issues or limitations that have not been addressed or fixed.
* Scalability : PyBrain may not be as scalable as other deep learning frameworks, particularly for training large-scale neural networks on massive datasets. Users working on high-performance computing or distributed training may need to consider alternative solutions.

* Compatibility : PyBrain's compatibility with Python 3.x and newer versions of other libraries may be limited. Users may need to make modifications or workarounds to use PyBrain with the latest versions of Python and related libraries.

* Community and Resources : PyBrain's community may be smaller compared to larger deep learning communities, leading to fewer resources, tutorials, or community support available online.