Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems, csMTL encoding of multiple task examples was developed and found to improve predictive performance for tasks that impoverished training sets. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of other ML models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning algorithms. Recent work on a modified neural network classifier for the WEKA data mining suite that accepts csMTL encoded data will be presented. And a new csMTL system that can transfer knowledge between image transformation tasks will also be demonstrated using some familiar facial images.