As work has come to require more dynamic and collaborative settings, activity-based work (ABW) environments have claimed increasing attention. However, without a clear understanding of office-workers' activity patterns the rash adoption of ABW may entail a variety of adverse effects, such as work-station shortages and inappropriate work-station arrangements. In this regard, the automated recognition of office activities with an accelerometer can help architects to understand activity patterns, thereby enabling effective space planning for the ABW environment. To the best of our knowledge, however, static office tasks requiring mainly manual activities have not yet been recognized. The study thus aims to determine the feasibility of recognizing seven static and non-static office activities simultaneously using an accelerometer. An experimental investigation was carried out to collect acceleration data from the seven activities. The accuracy of five classifiers (i.e. k-Nearest Neighbor, Discriminant Analysis, Support Vector Machine, Decision Tree and Ensemble Classifier), was analyzed with different window sizes. The highest classification accuracy, at 96.1%, was achieved by Ensemble Classifier, with a window size of 4.0 s. In addition, all office activities showed recall and precision greater than 0.9, demonstrating high prediction reliability. These findings help architects to understand static and non-static office activity patterns more systematically and comprehensively.