The number of output channels defines the number of kernels, which convolve over your input volume.
So even if your input image is a grayscale image (1 channel), a lot of different kernels can learn different things.
Have a look at Stanford’s CS231n course 221.
Here is an example of 96 learned kernels for a color image by Krizhevsky et al.:
          
            
           65

As you can see, the kernels learn a lot of different angles for edges etc.
Higher layers learn therefore more “complex” features based on the activations from previous layers.
I hope that clears thing up a bit.