Brain tumour besides being lethal can also affect other human organs on a long-term basis if not detected at an early stage. Based on the region of its presence and speed of growth, it can be classified as Glioma, Meningioma. or Pituitary. In the given work, a Stacked Neural Net approach for the classification of brain tumour is presented. The images are first pre-processed using Histogram equalization and One Hot Encoding. Thereby, the improved images are fed into 3 popular pre-trained deep networks viz. Res-Net-50, VGG-16, and Xception as level-0 models in parallel. The features extracted are then fed into a neural network as level 1 learner for the final classification. A publicly available Figshare dataset has been used for testing the performance. The suggested approach has been thoroughly evaluated against its base models using accuracy, precision, and recall metrics and also the performance has been analysed using accuracy and loss curves along with epochs. The suggested model achieved a validation and training accuracy of 97.3% and 98.12% respectively. It has been observed that the suggested model referred here as Stacked-Neural-Net outperforms when compared to the conventional deep learning methods and state-of-the-art methods. An improvement of 3.35%, 1.3% and 1.7% is achieved over Inception ResNetV1, a basic CNN and Alex-Net CNN models.