We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level concepts. Automatic speech recognition (ASR) and optical character recognition (OCR) are not used in the system. In the MED case with 100 example training videos, support-vector machines (SVM) are trained and fused to detect an event in the test set. In the case with 0 example videos, positive and negative concepts are extracted as keywords from the textual event description and events are detected with the high-level concepts. The MED results show that the SIFT keypoint descriptor is the one which contributes best to the results, fusion of multiple low-level features helps to improve the performance, and the textual event-description chain currently performs poorly. The TNO INS system presents a baseline open-source approach using standard SIFT keypoint detection and exhaustive matching. In order to speed up search times for queries a basic map-reduce scheme is presented to be used on a multi-node cluster. Our INS results show above-median results with acceptable search times.