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Indoor localization has received wide attention recently due to the potential use of wide range of intelligent services. This paper presents a deep learning-based approach for indoor localization by utilizing transmission channel quality metrics, including received signal strength (RSS) and channel state information (CSI). We partition a rectangular room plane into two-dimensional blocks. Each block is regarded as a class, and we formulate the localization as a classification problem. Using RSS and CSI, we develop four deep neural networks implemented with multi-layer perceptron (MLP) and one-dimensional convolutional neural network (1D-CNN) to estimate the location of a subject in a room. The experimental results indicate that the 1D-CNN using CSI information achieves excellent localization performance with much lower network complexity.