Transfer Learning methods aim to reuse previously acquired knowledge about a source task to facilitate learning of a target task. In this paper, we present a Meta Learning approach to find optimal hyperparameters for Transfer Learning processes given previously known metadata about the source task, the target task, and the pre-trained model. We collected metadata and model parameters from more than 15,000 Transfer Learning processes in a dataset, which we use to learn metamodels that predict a Transfer Learning process result in terms of accuracy on the validation sets, given prior information such as the number of epochs, learning rates, optimizers, etc. Using feedforward multilayer perceptrons (MLP), we show that and how our approach finds efficient hyperparameters for Transfer Learning for image classification.