Flow cytometry has emerged as a powerful tool for quantitative, single-cell analysis of both surface markers and intracellular antigens, including phosphoproteins and kinase signaling cascades, with the flexibility to process hundreds of samples in multiwell plate format. Quantitative flow cytometric analysis is being applied in many areas of biology, from the study of immunology in animal models or human patients to high-content drug screening of pharmacologically active compounds. However, these experiments generate thousands of data points per sample, each with multiple measured parameters, leading to data management and analysis challenges. We developed WebFlow (http://webflow.stanford.edu), a web server-based software package to manage, analyze, and visualize data from flow cytometry experiments. WebFlow is accessible via standard web browsers and does not require users to install software on their personal computers. The software enables plate-based annotation of large data sets, which provides the basis for exploratory data analysis tools and rapid visualization of multiple different parameters. These tools include custom user-defined statistics to normalize data to other wells or other channels, as well as interactive, user-selectable heat maps for viewing the underlying single-cell data. The web-based approach of WebFlow allows for sharing of data with collaborators or the general public. WebFlow provides a novel platform for quantitative analysis of flow cytometric data from high-throughput drug screening or disease profiling experiments.