We present a simple, psychologically plausible algorithm to perform unsupervised learning of morphemes. The algorithm is most suited to Indo-European languages with a concatenative morphology, and in particular English. We will describe the two approaches that work together to detect morphemes: 1) finding words that appear as substrings of other words, and 2) detecting changes in transitional probabilities. This algorithm yields particularly good results given its simplicity and conciseness: evaluated on a set of 532 human-segmented English words, the 252-line program achieved an F-score of 80.92% (Precision: 82.84% Recall: 79.10%).