Keep in mind - no one procedure is necessarily going to provide the "best result." Image interpretation is critical, both before and after classification. You will likely find urban areas misclassified as something else and non-urban areas classified as being urban.
You have two basic approaches:
1) Supervised classification: this involves selecting pixels from the image that you know represent urban areas. The selected algorithm will find pixels with similar spectral properties and assign them to the "urban" class. Note that urban areas are highly heterogeneous, meaning that any one "sample" may not be enough (mix of trees, roads, buildings, grass, etc.). You may want to select different types of urban areas and then aggregate them into one "urban area" class (i.e., begin by identifying urban residential, urban commercial, etc.).
2) Unsupervised classification: In this scenario, you choose a number of pixel "clusters" you want the algorithm to identify based on statistical similarity. You then examine each cluster and identify the type of land cover it represents. It is typically recommended that you choose a number of clusters which far exceeds the number of classes you eventually wish to end up with (you aggregate results). This has the advantage of helping you to identify the number of different urban settings that exist within your image (you may see one cluster of pixels that primarily makes up heavy industrial areas and another cluster that makes up residential areas). Note that these clusters will "overlap" in terms of class (i.e., several clusters may represent a single land cover type). If you find a cluster that includes pixels of more than one class (forest and urban residential), you may wish to create more clusters from that cluster - a technique known as "cluster busting."