This paper describes a proof-of-the-principle experiment in which maximum entropy learning is used for the automatic induction of shallow morphological features for the resource-scarce Bantu language of Gĩkũyũ. This novel approach circumvents the limitations of typical unsupervised morphological induction methods that employ minimum-edit distance metrics to establish morphological similarity between words. The experimental results show that the unsupervised maximum entropy learning approach compares favorably to those of the established AutoMorphology method.