We prove that log n-decision lists - the class of decision lists such that all their terms have low Kolmogorov complexity - are learnable in the simple PAC learning model. The proof is based on a transformation from an algorithm based on equivalence queries (found independently by Simon). Then we introduce the class of simple decision lists, and extend our algorithm to show that simple decision lists are simple-PAC learnable as well. This last result is relevant in that it is, to our knowledge, the first learning algorithm for decision lists in which an exponentially wide set of functions may be used for the terms. (This report supersedes LSI-95-2-R.)