In this paper, we propose an active learning based approach to event recognition in personal photo collections to tackle the challenges due to the weakly labeled data, and the presence of irrelevant pictures in personal photo collections. Conventional approaches relying on supervised learning can not identify the relevant samples in training albums, often leading to wrong classification. In our work, we aim to utilize the concepts of active learning to choose the most relevant samples from a collection and train a classifier. We also investigate the importance of relevant images in the event recognition process, and show how the performance degrades if all images from an album, containing the irrelevant ones, are included in the process. The experimental evaluation is carried out on a benchmark dataset composed of a large number of personal photo albums. We demonstrate that the proposed strategy yields encouraging scores in the presence of irrelevant images in personal photo collections, advancing recent leading works.