It is easiest to think about interactions in terms of discrete variables. Perhaps you might have studied two-way ANOVAs, where we have two grouping variables (e.g. gender and age category, with three levels for age) and are looking at how they pertain to some continuous measure (our dependent variable, e.g. IQ).
The x1 * x2 term, if significant, can be understood (in this trivial, made-up example) as IQ behaving differently across the levels of age for the different genders. For example, maybe IQ is stable for males across the three age groups, but young females start below young males and have an upward trajectory (with the old age group having a higher mean than the old age group for males). In a means plot, this would imply a horizontal line for males in the middle of the graph, and perhaps a 45 degree line for females that starts below males but ends above males.
The gist is that as you move along the levels of one variable (or "holding X1 constant"), what is going on in the other variable changes. This interpretation also works with continuous predictor variables, but is not so easy to illustrate concretely. In that case, you might want to take particular values of X1 and X2 and see what happens to Y.