Massively parallel applications must address problems that will be too large for workstations for the next several years, or else it will not make sense to expend development costs on them. Suitable applications include one or more of the following properties: 1) large amounts of data; 2) intensive computations; 3) requirement for very fast response times; 4) ways to trade computations for human effort, as in developing applications using learning methods. Most of the suitable applications that we have found come from the general area of very large databases. Massively parallel machines have proved to be important not only in being able to run large applications, but in accelerating development (allowing the use of simpler algorithms, cutting the time to test performance on realistic databases) and allowing many different algorithms and parameter settings to be tried and compared for a particular task. This presentation summarizes four such applications. The applications described are: 1) prediction of credit card "defaulters" (non-payers) and "attritters" (people who didn’t renew their cards) from a credit card database; 2) prediction of continuation of time series, e.g. stock price movements; 3) automatic keyword assignment for news articles; and 4) protein secondary structure prediction. These add to a llst identified in an earlier paper [Waltz 90] including: 5) automatic classification of U.S. Census Bureau long forms, using MBR -- Memory-Based Reasoning generating catalogs for a mail order company that maximize expected net returns (revenues from orders minus cost of the catalogs and mailings) using geneticaily-inspired methods; and 7) text-based intelligent systems for information retrieval, decision support, etc.