The increasingly ubiquitous global information structure makes it possible to examine high-volume text streams that contain documents written in a variety of languages. Present monolingual adaptive filtering techniques learn profiles which reflect user preferences and then apply those profiles to reduce the volume of new documents that must be examined by the user to manageable levels. This paper presents three techniques for extending adaptive monolingual text filtering techniques to manage multilingual document streams. Experimental results are given which demonstrate that dictionary-based and corpus-based techniques achieve similar performance in this application. This observation motivates our development of a translation technique designed specifically for vector space text representations which can in principle exploit both dictionary-based and corpus-based techniques. Results of initial experiments with this technique are given and the potential advantages of the new technique are discussed. The paper concludes with a discussion of future directions for adaptive multilingual text filtering.