This thesis investigates sound event localization and detection (SELD), which is an emerging research topic in audio signal processing and machine learning. SELD aims to identify and localize multiple overlapping sounds in an acoustic environment, which has many important applications in audio surveillance, robotic auditory systems, and human-machine interaction. The main challenges of SELD are reverberation, noise, and source variability. The goal of this thesis is to compare different machine learning algorithms and architectures that can solve SELD, focusing on deep neural networks (DNNs). The research questions are: how do different types of DNNs perform on SELD? What are the pros and cons of each type of DNN? How do DNNs perform under different noisy conditions? The methodology consists of training and testing DNN models on the same dataset, which contains recordings from first-order ambisonics microphones.