In this thesis, the indoor positioning problem is approached using an Android platform. This approach is based in tests with a set of algorithms and techniques. The performance of each algorithm and technique, in an indoor environment, is measured using data from a dataset of a known location mapped with a smartphone in a static and dynamic context, enabling the possibility to perform a wide range of tests and get useful insights. This thesis approach uses a developed dataset to test the performance of a set of algorithms in different contexts using the Android platform. The dataset has information relative to Wi-Fi signal strength, acceleration and magnetic field strength in different locations inside the room. Algorithms were tested using the useful information provided by the dataset. After those tests, it was concluded that fingerprinting is more suitable than multilateration indoors since multilateration can become unstable due its need of a distance estimation. The algorithm that returned better results was the Minimum Mean Square Error (MMSE) algorithm. Using this algorithm, a functional Android indoor positioning application was built.