Indoor localization is a hot topic since the demand for Location Based Services(LBS) has increased, especially (semi) public places like museums, office buildings and congress halls would benefit from LBS. Outdoor localization and its applications like navigation systems are well implemented in society and used by many on a daily basis. Applications using indoor localization are underrepresented due to the lack of a robust and scalable indoor localization technique. Attempting to solve this problem, first, a suitable indoor localization technique (hardware) is selected and second, an indoor localization method (algorithm) is applied. For the localization technique two radio propagation techniques are tested in this research:first an established technique called Ultra High Frequency Radio Frequency Identification (UHF RFID) and second, a newly arrived technique called Bluetooth Low Energy (BLE). Both techniques use a sensor and tag. In the case of UHF RFID the person to be located carries a tag and is recorded by sensors on the ceiling. In the case of BLE the person to be located holds the sensor in hand and senses the tags on the ceiling. Results show that the UHF RFID is highly sensitive to environmental changes and water bodies (including the user itself). This makes the proximity indication of the technique unpredictable and the technique difficult to use in various locations with a large amount of users, which is often the case in (semi) public places. The BLE shows quite stable results in terms of its sensitivity to interferences and shows a clear correlation between distance and signal strength. The latter two characteristics make it a suitable technique for indoor localization. Two localization algorithms using BLE are proposed; first a dependent algorithm which takes into account the specific characteristics of the sensor, and second, an independent algorithm (referred to as PML) which functions independently from the type of sensor used. The latter is an advantage because the sensor is incorporated in the mobile phone of the user. Especially when there are potentially multiple users holding different types of mobile phones, the sensors can vary widely. The dependent algorithm is based on a probability function using a basic concept of trilateration dependent on the measurements of at least three beacons. PML is based on the ratio between measurements of at least three beacons. Results of both algorithms show in most cases a computed location within a meter of the actual location. Generally, the dependent algorithm shows slightly better results with regards to the PML method . However, the practical usability and scalability of the PML methodmakes it preferable over the dependent algorithm.